WO2019232843A1

WO2019232843A1 - Handwritten model training method and apparatus, handwritten image recognition method and apparatus, and device and medium

Info

Publication number: WO2019232843A1
Application number: PCT/CN2018/094168
Authority: WO
Inventors: 高梁梁; 周罡
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-06-04
Filing date: 2018-07-03
Publication date: 2019-12-12
Also published as: CN108985297A

Abstract

Disclosed are a handwritten model training method and apparatus, a handwritten image recognition method and apparatus, and a device and a medium. The handwritten model training method comprises: obtaining a training handwritten Chinese image; performing single font cutting on the training handwritten Chinese image by using a vertical projection method, to obtain training single font images; dividing the training single font images into a training set and a test set according to a preset proportion; sequentially annotating the training single font images in the training set, inputting the annotated training single font images into a long-short time memory neural network for training, and updating network parameters of the long-short time memory neural network by using a batch gradient descent algorithm to obtain an original handwritten character recognition model; and testing the original handwritten character recognition model by using the training single font images in the test set, and when the test accuracy is greater than the preset accuracy, obtaining a target handwritten character recognition model. The handwritten model training method has high training efficiency and high recognition accuracy.

Description

Handwriting model training, handwriting image recognition method, device, equipment and medium

This patent application is based on a Chinese invention patent application filed on June 4, 2018 with application number 201810564693.5, entitled "Handwriting Model Training, Handwritten Image Recognition Method, Device, Equipment, and Medium", and claims its priority.

Technical field

The present application relates to the field of image recognition, and in particular, to a method, a device, a device, and a medium for handwriting model training and handwriting image recognition.

Background technique

With the development of the information age, artificial intelligence technology is increasingly used as a core technology to solve specific problems in people's lives. At present, when recognizing handwritten Chinese character images, because the structure of Chinese characters is relatively complex, such as "魍, 魉", and there are many similarly structured characters in Chinese characters, such as "Jinhe Ling", the recognition accuracy rate will appear Lower case. OCR (optical character recognition) technology can be used to identify standard, simple and standardized sentences, but for handwritten sentences, because everyone's writing habits are different and not standard Chinese characters, when using OCR technology to recognize , Will reduce the recognition accuracy and affect the recognition of handwritten Chinese characters.

Summary of the Invention

Based on this, it is necessary to provide a method, a device, a device, and a medium for training a handwriting model and a handwritten image for the above technical problems.

A handwriting model training method includes:

Obtain training handwritten Chinese images;

Performing a single font cutting on the training handwritten Chinese image by using a vertical projection method to obtain a training single font image;

Dividing the training single font image into a training set and a test set according to a preset ratio;

Sequentially label the training single font images in the training set, and input the labeled training single font images into a long-term and short-term memory neural network for training, and use a batch gradient descent algorithm to network parameters of the long-term and short-term memory neural network Update to get the original handwriting recognition model;

The original handwriting recognition model is tested by using the training single font image in the test set, and when the test accuracy is greater than a preset accuracy rate, a target handwriting recognition model is obtained.

A handwriting model training device includes:

Training handwritten Chinese image acquisition module for acquiring training handwritten Chinese images;

A training handwritten Chinese image division module, configured to divide the trained handwritten Chinese image into a training set and a test set according to a preset ratio;

A training single font image acquisition module, configured to use a vertical projection method to perform single font cutting on the training handwritten Chinese image to obtain a training single font image;

The original handwriting recognition model acquisition module is used to sequentially label the training single font images in the training set, and input the labeled single font images into the long-term and short-term memory neural network for training. The network parameters of the long-term and short-term memory neural network are updated to obtain the original handwriting recognition model;

A target handwriting recognition model acquisition module is configured to test the original handwriting recognition model using a training single font image in the test set, and obtain a target handwriting recognition model when a test accuracy rate is greater than a preset accuracy rate.

A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the following steps are implemented:

Obtain training handwritten Chinese images;

One or more non-volatile readable storage media storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the following steps:

Obtain training handwritten Chinese images;

A handwritten image recognition method includes:

Obtaining an image to be identified, where the image to be identified includes handwritten Chinese characters and background pictures;

Preprocessing the image to be identified to obtain an original image;

Processing the original image using a kernel density estimation algorithm, removing the background picture, and obtaining a target image including the handwritten Chinese character;

Performing a single font cutting on the target image using a vertical projection method to obtain a single character image to be recognized;

The image of the to-be-recognized word is input to a target handwriting recognition model for recognition, and a handwritten Chinese character corresponding to each of the to-be-recognized image is obtained; wherein the target handwriting recognition model is obtained by using the handwriting model training method.

A handwritten image recognition device includes:

A to-be-recognized image acquisition module, configured to obtain an to-be-recognized image, where the to-be-recognized image includes handwritten Chinese characters and a background picture;

An original image acquisition module, configured to preprocess the image to be identified to obtain an original image;

A target image acquisition module, configured to process the original image by using a kernel density estimation algorithm, remove the background picture, and obtain a target image including the handwritten Chinese character;

A to-be-recognized single-word image acquisition module, configured to use a vertical projection method to perform single-font cutting on the target image to obtain the to-be-recognized single-word image;

A handwritten Chinese character acquisition module is configured to input an image of a single character to be recognized into a target handwriting recognition model for recognition, and obtain a handwritten Chinese character corresponding to each of the single character images to be recognized; wherein the target handwriting recognition model uses the handwriting model Obtained by the training method.

Preprocessing the image to be identified to obtain an original image;

Details of one or more embodiments of the present application are set forth in the accompanying drawings and description below. Other features and advantages of the application will become apparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions of the embodiments of the present application more clearly, the drawings used in the description of the embodiments of the application will be briefly introduced below. Obviously, the drawings in the following description are just some embodiments of the application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without paying creative labor.

FIG. 1 is an application scenario diagram of a handwriting model training method or a handwriting image recognition method according to an embodiment of the present application;

2 is a flowchart of a handwriting model training method according to an embodiment of the present application;

FIG. 3 is a specific flowchart of step S14 in FIG. 2;

4 is a schematic diagram of a handwriting model training device according to an embodiment of the present application;

5 is a flowchart of a handwritten image recognition method according to an embodiment of the present application;

6 is a specific flowchart of step S22 in FIG. 5;

FIG. 7 is a specific flowchart of step S23 in FIG. 5;

8 is a specific flowchart of step S234 in FIG. 7;

9 is a schematic diagram of a handwritten image recognition device according to an embodiment of the present application;

FIG. 10 is a schematic diagram of a computer device according to an embodiment of the present application.

Detailed ways

In the following, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

The handwriting model training method provided in the embodiment of the present application can be applied in the application environment as shown in FIG. 1. The application environment of the handwriting model training method includes a server and a computer device, wherein the computer device communicates with the server through a network, and the computer device is a device that can interact with a user, including, but not limited to, a computer, a smartphone, and a tablet. . The handwriting model training method provided in the embodiment of the present application is applied to a server.

In an embodiment, as shown in FIG. 2, a handwriting model training method is provided. The method is applied to the server in FIG. 1 as an example for description, and includes the following steps:

S11: Obtain training handwritten Chinese images.

The training handwritten Chinese image is a sample image collected from an open source library for model training in advance. The training handwritten Chinese image includes N (N is a positive integer) handwriting samples corresponding to each Chinese in the Chinese secondary word library. The Chinese secondary character library is a very useful Chinese character library that is coded in the order of radical strokes of Chinese characters. Specifically, N handwriting samples handwritten by different people in the open source library are collected to enable the server to obtain training handwritten Chinese images. Because different users have different writing habits, N handwriting samples (that is, training handwritten Chinese images) are used for Training greatly improves the generalization of the model.

S12: Use a vertical projection method to perform single font cutting on the training handwritten Chinese image to obtain a training single font image.

Among them, the vertical projection method refers to a method of vertically projecting each line of handwritten Chinese characters to obtain a vertical projection histogram. The vertical projection histogram refers to the number of pixels reflecting the target image in the vertical direction.

Specifically, using a vertical projection method to perform single font cutting on the training handwritten Chinese image specifically includes the following steps: the server scans at least one row of handwritten Chinese characters in the training handwritten Chinese image line by line, and obtains the pixel value corresponding to each line of handwritten Chinese characters. The vertical projection histogram corresponding to one pixel value is used to obtain the number of pixels corresponding to different pixel values. According to the minimum value in the vertical projection histogram, the training handwritten Chinese image is cyclically cut to obtain a training single font image. Understandably, the pixel value corresponding to each handwritten Chinese character is relatively concentrated, and the pixel value corresponding to the gap between the Chinese character and the Chinese character is relatively sparse. The density of the corresponding pixel value is reflected in the corresponding vertical projection histogram. In the vertical projection histogram, the number of pixels corresponding to pixel values with Chinese characters is relatively high, and the number of pixels corresponding to pixel values without Chinese characters is relatively low. The vertical projection method can effectively perform single font cutting on the training handwritten Chinese image to obtain a training list. Font image, easy to implement and improve training efficiency.

S13: Divide the training single font image into a training set and a test set according to a preset ratio.

Among them, the training set is a learning sample data set, which is to establish a classifier by matching some parameters, that is, training the machine learning model using the target training text data in the training set to determine the parameters of the machine learning model. A test set is used to test the discrimination capabilities of a trained machine learning model, such as accuracy. The preset ratio is a preset ratio for dividing the training handwritten Chinese image. In this embodiment, the training single font image may be divided according to a ratio of 9: 1, that is, 90% of the training single font image is used as the training set, and the remaining 10% of the training single font image is used as the test set.

S14: sequentially label the training single font images in the training set, and input the labeled training single font images into the long-term and short-term memory neural network for training, and use a batch gradient descent algorithm to update the network parameters of the long-term and short-term memory neural network. To get the original handwriting recognition model.

Among them, the original handwriting recognition model is a model obtained through multiple iterations of long-term and short-term memory neural networks. Long-short-term memory neural (LSTM) network is a kind of time-recursive neural network, which is suitable for processing and predicting important events with time series and time series with relatively long intervals and delays.

Specifically, the server performs labeling according to the chronological order of the training single font images, and inputs the labeled training single font images into the target handwriting recognition model for training to obtain the original handwriting recognition model. Understandably, each training single font image is arranged in order. For example, the original image is "I am very happy today", then each training single font image can be labeled with Arabic numerals from left to right, that is, "present (1) Day (2) Very (3) Open (4) Heart (5) ", so that the training single font image has timeliness, so that the original handwriting recognition model can be trained in connection with the context and improve the accuracy of the model.

The batch gradient descent algorithm is to update all the samples in the training set (training font image) every time the network parameters are updated, which can obtain the global optimal solution and improve the accuracy of the model. The network parameters are the weights and offsets between the layers of the long- and short-term memory neural network. The long-term and short-term memory neural network has the function of time memory, so it is used to process the training single font image carrying the time series state.

The long-short-term memory neural network has a network structure of an input layer, at least one hidden layer, and an output layer. The input layer is the first layer of the long-term and short-term memory neural network, which is used to receive external signals, that is, it is responsible for receiving training single font images. The output layer is the last layer of the long-term and short-term memory neural network, which is used to output signals to the outside world, that is, it is responsible for outputting the calculation results of the long-term and short-term memory neural network. Hidden layers are layers other than the input layer and the output layer in the long-term and short-term memory neural network, which are used to process the training single font image and obtain the calculation results of the long-term and short-term memory neural network. Understandably, the use of long-term and short-term memory neural networks for model training increases the timeliness of the training single font image, so that the training single font image is trained according to the context, thereby improving the accuracy of the target handwriting recognition model. In this embodiment, the output layer of the long-term and short-term memory neural network uses Softmax (regression model) for regression processing, and is used to classify the output weight matrix. Softmax (regression model) is a classification function commonly used in neural networks. It maps the output of multiple neurons into the [0,1] interval, which can be understood as a probability. It is simple and convenient to calculate, so as to perform multi-classification. Output to make its output more accurate.

In an embodiment, as shown in FIG. 3, in step S14, the training single font image is sequentially labeled, and the labeled single font image is input to a long-term and short-term memory neural network for training to obtain target handwriting recognition. The model includes the following steps:

S141: In the hidden layer of the long-term and short-term memory neural network, the training single font image is processed by using the first activation function to obtain a neuron carrying an activation state identifier.

Among them, each neuron in the hidden layer of the long-term and short-term memory neural network includes three gates, which are an input gate, a forgetting gate, and an output gate, respectively. The forget gate determines the past information to be discarded in the neuron. The input gate determines the information to be added to the neuron. The output gate determines the information to be output in the neuron. The first activation function is a function for activating a neuron state. The state of the neuron determines the information discarded, added, and output by each gate (ie, input gate, forget gate, and output gate). The activation status flag includes a pass flag and a fail flag. The identifiers corresponding to the input gate, the forget gate, and the output gate in this embodiment are i, f, and o, respectively.

In this embodiment, the Sigmoid (S-shaped growth curve) function is specifically selected as the first activation function. The Sigmoid function is a S-shaped function common in biology. In information science, due to its single increase and inverse function single increase In other properties, the Sigmoid function is often used as a threshold function for neural networks, mapping variables between 0 and 1. The calculation formula for the first activation function is

Among them, z represents the output value of the forget gate.

Specifically, the forgetting gate includes a forgetting threshold. By calculating the activation state of each neuron (training font image), a neuron carrying an activation state identifier as a pass identifier is obtained. Among them, the calculation formula of the forgetting gate is f _t = σ (W _f · [h _t-1 , x _t ] + b _f ) to calculate which information of the forgetting gate is received (that is, only the neurons carrying the activation status flag as the pass flag are received). ), F _t represents the forgetting threshold (that is, the activation state), W _f represents the weight matrix of the forgetting gate, b _f represents the weight bias term of the forgetting gate, h _t-1 represents the output of the neuron at the previous moment, and x _t represents The input data at the current time (that is, the training single font image), t represents the current time, and t-1 represents the previous time. The forgetting gate also includes the forgetting threshold. The calculation of the font image of the training single through the calculation formula of the forgetting gate will obtain a scalar in the range of 0-1. This scalar determines the past information received by the neuron based on the comprehensive judgment of the current state and the past state. To achieve data reduction, reduce the amount of calculation, and improve training efficiency.

S142: In the hidden layer of the long-term and short-term memory neural network, a second activation function is used to process the neurons carrying the identification of the activation state to obtain the output value of the hidden layer of the long-term and short-term memory neural network.

The output value of the hidden layer of the long-term and short-term memory neural network includes the output value of the input gate, the output value of the output gate, and the state of the neuron. Specifically, in the input gate in the hidden layer of the long-term and short-term memory neural network, a second activation function is used to carry the activation state identifier to perform calculation through the identified neurons to obtain the output value of the hidden layer. In this embodiment, because the expressive ability of the linear model is insufficient, a tanh (hyperbolic tangent) function is used as the activation function of the input gate (ie, the second activation function). Non-linear factors can be added to make the trained target handwriting recognition model Able to solve more complex problems. In addition, the activation function tanh (hyperbolic tangent) has the advantage of fast convergence speed, which can save training time and increase training efficiency.

Specifically, the output value of the input gate is calculated by a calculation formula of the input gate. The input gate also includes an input threshold. The calculation formula of the input gate is i _t = σ (W _i · [h _t-1 , x _t ] + b _i ), where W _i is a weight matrix of the input gate, i _t represents the input threshold, and b _i represents the offset term of the input gate. The calculation of the font image of the training single through the calculation formula of the input gate will obtain a 0-1 interval scalar (that is, the input threshold). This scalar controls the nerve. Yuan judges the proportion of the received current information, that is, the proportion of newly input information, according to the comprehensive evaluation of the current state and the past state, so as to reduce the calculation amount and improve the training efficiency.

Then, the calculation formula of the state of the neuron is adopted.

with

Calculate the current neuron state; where W _c represents the weight matrix for calculating the unit state, and b _c represents the bias term for the unit state,

Represents the state of the neuron at the previous moment, and C _t represents the state of the neuron at the current moment. By performing a dot product operation on the state of the neuron and the forgetting threshold (input threshold), the model can only output the required information, thereby improving the efficiency of model learning.

Finally, the output gate calculation formula o _t = σ (W _o [h _t-1 , x _t ] + b _o ) is used to calculate which information is output in the output gate, and then the formula h _t = o _t * tanh (C _t ) Calculate the output value of the neuron at the current moment, where o _t represents the output threshold, W _o represents the weight matrix of the output gate, _bo represents the bias term of the output gate, and h _t represents the output value of the neuron at the current moment.

S143: According to the output value of the hidden layer of the long-term and short-term memory neural network, a batch gradient descent algorithm is used to update the network parameters of the long-term and short-term memory neural network to obtain a target handwriting recognition model.

Among them, the network parameters of the long-term and short-term memory neural network refer to the weights and offsets between the layers of the long-term and short-term memory neural network. The formula of the batch gradient descent algorithm is:

with

Among them, J (θ) is the loss function, m is the number of single font images in the training set, θ _j is the network parameter of the j-th layer of the short-term memory neural network, and h _θ (x) is the number of hidden layers of the short-term memory neural network. The output value, (x ⁱ , y ⁱ ) represents the i-th training single font image.

First, build a formula based on the loss function

Construct a loss function. By formula

Perform partial derivative operations on the loss function to update the network parameters, that is, the weights and offsets between the layers, and apply the updated weights and offsets of the layers to the long-term and short-term memory neural network. A target handwriting recognition model can be obtained.

Further, each weight in the target handwriting recognition model implements the functions of the target handwriting recognition model to decide which old information to discard, which new information to add, and which information to output. The output layer of the target handwriting recognition model will eventually output a probability value, which refers to the probability of the corresponding Chinese character being recognized by the training single font image. It can be widely used in handwriting recognition to accurately recognize the training single font image. purpose.

S15: The original handwriting recognition model is tested using the training single font image in the test set, and the target handwriting recognition model is obtained when the test accuracy is greater than a preset accuracy rate.

Specifically, in step S15, all the training single font images in the test set are input to the original handwriting recognition model for testing, and the test accuracy rate is obtained (that is, the number of accurate prediction results of all original handwriting recognition models is divided by all training single font images in the training set quantity). Then judge whether the test accuracy rate is greater than the preset accuracy rate. If the test accuracy rate is greater than the preset accuracy rate, the original handwriting recognition model is deemed to be more accurate, and the original handwriting recognition model is used as the target handwriting recognition model; otherwise, If the test accuracy rate is not greater than the preset accuracy rate, the prediction result of the original handwriting recognition model is deemed to be inaccurate. It is still necessary to use steps S11-S14 for training, and then test again until the test accuracy rate reaches the preset accuracy rate. , Stop training, and further improve the accuracy of the target handwriting recognition model.

In this embodiment, a training handwritten Chinese image is first obtained, a single font cutting is performed on the training handwritten image using a vertical projection method, a training single font image is obtained, and the training single font image is divided into a training set and a test set according to a preset ratio, In order to sequentially label the training single font images in the training set, so that the training single font images have timing. The labeled training single font image is input into the long-term and short-term memory neural network for training. According to the time series of the training single-font image, the long-term and short-term memory neural network trains the training single-font image according to the context. The network parameters of the long and short-term memory neural network are updated to obtain the original handwriting recognition model, thereby improving the accuracy of the model. Finally, the original handwriting recognition model is tested with the training single font image in the test set. When the test accuracy is greater than the preset accuracy rate, the target handwriting recognition model is obtained, which further improves the accuracy of the target handwriting recognition model.

In one embodiment, a handwriting model training device is provided. The handwriting model training device corresponds to the handwriting model training method in the above embodiment. As shown in FIG. 4, the handwriting model training device includes a training handwritten Chinese image acquisition module 11, a training handwritten Chinese image division module 12, a training single font image acquisition module 13, an original handwriting recognition model acquisition module 14 and a target handwriting recognition model. The acquisition module 15 is detailed as follows:

A training handwritten Chinese image acquisition module 11 for acquiring a training handwritten Chinese image;

A training handwritten Chinese image division module 12 for dividing the trained handwritten Chinese image into a training set and a test set according to a preset ratio;

A training single font image acquisition module 13 is configured to use a vertical projection method to perform single font cutting on a training handwritten Chinese image to obtain a training single font image;

The original handwriting recognition model acquisition module 14 is used to sequentially label the training single font images in the training set, and input the labeled single font images to the long-term and short-term memory neural network for training. The network parameters of the memory neural network are updated to obtain the original handwriting recognition model;

The target handwriting recognition model acquisition module 15 is used to test the original handwriting recognition model using a training single font image in a test set. When the test accuracy is greater than a preset accuracy rate, the target handwriting recognition model is obtained.

Specifically, the original handwriting recognition model acquisition module 14 includes an activation state neuron acquisition unit 141, a hidden layer output value acquisition unit 142, and a target recognition model acquisition unit 143.

The activation state neuron acquisition unit 141 is configured to process the training single font image by using a first activation function in a hidden layer of the long-term and short-term memory neural network to acquire a neuron carrying an activation state identifier.

The hidden layer output value obtaining unit 142 is configured to process the neuron carrying the activation state identifier in the hidden layer of the long-term and short-term memory neural network to obtain the output value of the hidden layer of the long-term and short-term memory neural network.

The target recognition model acquisition unit 143 is configured to update the network parameters of the long-term and short-term memory neural network by using a batch gradient descent algorithm according to the output value of the hidden layer of the long-term and short-term memory neural network to obtain a target handwriting recognition model.

Specifically, the formula of the batch gradient descent algorithm is:

with

For the specific limitation of the handwriting model training device, refer to the foregoing limitation on the handwriting model training method, which will not be repeated here. Each module in the above handwriting model training device may be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the hardware in or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. 10. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in a non-volatile storage medium. The database of the computer device is used to store data generated or obtained during the execution of the training method of the handwriting model, such as the target handwriting recognition model. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by one or more processors, the one or more processors are executed to implement a handwriting model training method.

In one embodiment, a computer device is provided, which includes a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor. When the processor executes the computer-readable instructions, the processor implements the following steps: obtaining training Handwritten Chinese images; using the vertical projection method to perform single font cutting on the training handwritten Chinese images to obtain training single font images; dividing the training single font images into training sets and test sets according to a preset ratio; and performing training single font images on the training set. Sequential labeling, and input the labeled training single font image into the long-term and short-term memory neural network for training. The batch gradient descent algorithm is used to update the network parameters of the long-term and short-term memory neural network to obtain the original handwriting recognition model. The test set is used. The training single font image tests the original handwriting recognition model. When the test accuracy is greater than a preset accuracy rate, the target handwriting recognition model is obtained.

In an embodiment, when the processor executes the computer-readable instructions, the following steps are further implemented: the hidden layer of the long-term and short-term memory neural network uses the first activation function to process the training single font image to obtain a neuron carrying an activation state identifier; In the hidden layer of the long-term and short-term memory neural network, the second activation function is used to process the neurons carrying the identification of the activation state to obtain the output value of the hidden layer of the long-term and short-term memory neural network. The batch gradient descent algorithm updates the network parameters of the long-term and short-term memory neural network to obtain the target handwriting recognition model.

Specifically, the formula of the batch gradient descent algorithm is:

with

In one embodiment, a non-volatile storage medium is provided on which computer-readable instructions are stored, and the computer-readable instructions, when executed by one or more processors, cause the one or more processes to be processed. The following steps are performed during the execution of the device: obtaining a training handwritten Chinese image; using a vertical projection method to perform single font cutting on the training handwritten Chinese image to obtain a training single font image; dividing the training single font image into a training set and a test set according to a preset ratio; Sequentially label the training single font images in the training set, and input the labeled training single font images into the long-term and short-term memory neural network for training. The batch gradient descent algorithm is used to update the network parameters of the long-term and short-term memory neural network to obtain The original handwriting recognition model; the original handwriting recognition model is tested with the training single font image in the test set, and the target handwriting recognition model is obtained when the test accuracy is greater than a preset accuracy rate.

In one embodiment, when the computer-readable instructions are executed by one or more processors, the execution of the one or more processors further implements the following steps: a first layer is used in the hidden layer of the short-term memory neural network; The activation function processes the training single font image to obtain the neurons carrying the identification of the active state; in the hidden layer of the short-term memory neural network, the second activation function is used to process the neurons carrying the identification of the active state to obtain the long-term memory neural network The output value of the hidden layer; based on the output value of the hidden layer of the long-term and short-term memory neural network, the batch gradient descent algorithm is used to update the network parameters of the long-term and short-term memory neural network to obtain the target handwriting recognition model.

Specifically, the formula of the batch gradient descent algorithm is:

with

In an embodiment, as shown in FIG. 5, a handwritten image recognition method is provided. The method is applied to the server in FIG. 1 as an example for description, and includes the following steps:

S21: Acquire an image to be identified. The image to be identified includes handwritten Chinese characters and a background picture.

The image to be identified is an unprocessed image containing handwritten Chinese characters collected by an acquisition module on a computer device. The image to be recognized includes handwritten Chinese characters and background pictures. The background picture is a noise picture other than handwritten Chinese characters in the image to be identified. Noise pictures are pictures that interfere with handwritten Chinese characters. In this embodiment, a user may collect an image to be identified containing handwritten Chinese characters and upload it to a server through a collection module on a computer device, so that the server obtains the image to be identified. The acquisition module includes but is not limited to camera shooting and local upload.

S22: Pre-process the image to be identified to obtain the original image.

Among them, the original image is an image obtained by pre-processing the image to be identified and excluding interference factors. Specifically, since the image to be identified may contain multiple interference factors, such as multiple colors, it is not conducive to subsequent identification. Therefore, the image to be identified needs to be pre-processed to obtain the original image that excludes interference factors. The original image can be understood as the image obtained after the background image is excluded from the image to be identified.

In an embodiment, as shown in FIG. 6, in step S22, the image to be identified is pre-processed to obtain the original image, which specifically includes the following steps:

S221: Enlarge and grayscale the image to be identified to obtain a grayscale image.

The grayscale image is a grayscale image obtained after the image to be identified is enlarged and processed for grayscale processing. The grayed image includes a matrix of pixel values. The pixel value matrix refers to a matrix containing pixel values corresponding to each pixel in an image to be identified. In this embodiment, the server uses the imread function to read the pixel value of each pixel in the image to be identified, and performs enlargement and grayscale processing on the image to be identified to obtain a grayscale image. The imread function is a function in computer language for reading pixel values in an image file. The pixel value is a value assigned by the computer when the original image is digitized.

The image to be identified may contain multiple colors, and the color itself is very susceptible to factors such as light. There are many changes in the color of similar objects, so it is difficult for the color itself to provide key information. Therefore, graying processing is required for the image to be identified. In order to eliminate interference, reduce the complexity of the image and the amount of information processing. However, if the size of the handwritten Chinese characters in the image to be identified is small, if the grayscale processing is directly performed, the thickness of the strokes of the handwritten Chinese characters will be too small and will be excluded as interference items. Therefore, in order to increase the thickness of the text strokes, Enlarge the image to be identified, and then perform grayscale processing to avoid the grayscale processing directly, which leads to the problem that the thickness of strokes of handwritten Chinese characters is too small and excluded as interference items.

Specifically, the server enlarges the original image according to the following formula: x → x ^r , where x represents an element in the matrix M, r is the number of times, and the changed element x ^{r is} replaced with x in the pixel value matrix M.

The gradation process is a process in which the image to be identified presents a noticeable black and white effect. Specifically, performing grayscale processing on the enlarged image includes: the color of each pixel in the image to be identified is determined by three components of R (red), G (green), and B (blue), and each Each component has 256 values from 0 to 255 (0 is the darkest, and 255 is the brightest, white). The grayscale image is a special color image with the same three components of R, G, and B. In this embodiment, the server can directly use the imread function to read the image to be identified, and the specific values of the three components of R, G, and B corresponding to each pixel in the grayscale image can be obtained.

S222: Standardize the grayscale image to obtain the original image.

Among them, the standardization process refers to a process of performing a standard transformation process on a grayscale image to transform it into a fixed standard form. Specifically, because the pixel values of each pixel in the grayscale image are scattered, the magnitude of the data is not uniform, which will affect the accuracy of subsequent model recognition. Therefore, the grayscale image needs to be standardized to uniformize the magnitude of the data. .

Specifically, the server standardizes the grayscale image by using a formula for normalization processing to avoid the problem that the pixel values in the grayscale image are scattered and the order of data is not uniform. Among them, the standardization formula is

X is the pixel value of the grayed image M, X ′ is the pixel value of the original image, M _min is the smallest pixel value in the grayed image M, and M _max is the largest pixel value in the grayed image M.

S23: Use the kernel density estimation algorithm to process the original image, remove the background image, and obtain a target image including handwritten Chinese characters.

Among them, the kernel density estimation algorithm (kernel density estimation) is a non-parametric method that studies the data distribution characteristics from the data sample itself to estimate the probability density function. The target image refers to an image that contains only handwritten Chinese characters by processing the original image using a kernel density estimation algorithm. Specifically, the server uses a kernel density estimation algorithm to process the original image to eliminate background image interference and obtain a target image including handwritten Chinese characters.

Specifically, the calculation formula of the kernel density estimation algorithm is

Among them, K (.) Is the kernel function, h is the pixel value range, x is the pixel value of the pixel whose probability density is to be estimated, x _i is the i-th pixel value in the h range, and n is the pixel value x in the h range. Number of

Represents the estimated probability density of a pixel.

In an embodiment, as shown in FIG. 7, in step S23, the original image is processed by using a kernel density estimation algorithm to remove the background image to obtain a target image including handwritten Chinese characters, which specifically includes the following steps:

S231: Perform statistics on pixel values in the original image to obtain a histogram of the original image.

The original image histogram is a histogram obtained by statistically calculating pixel values in the original image. Histogram (Histogram) is a kind of statistical report diagram that represents the distribution of data by a series of vertical stripes or line segments of varying heights. In this embodiment, the horizontal axis of the histogram of the original image represents pixel values, and the vertical axis represents the appearance frequency corresponding to the pixel values. The server obtains the histogram of the original image by counting the pixel values in the original image, so that it can intuitively see the distribution of the pixel values in the original image, and provides technical support for subsequent Gaussian kernel density estimation algorithms.

S232: The original image histogram is processed by using a Gaussian kernel density estimation algorithm to obtain at least one frequency maximum and at least one frequency minimum corresponding to the original image histogram.

Among them, the Gaussian kernel density estimation algorithm refers to a kernel density estimation method in which the kernel function is a Gaussian kernel function. The formula of the Gaussian kernel function is

Among them, K _(x) refers to a Gaussian kernel function whose pixels (independent variables) are x, x refers to the pixel value in the original image, and e and π are constants. Frequency maxima refer to the maxima at different frequency intervals in the frequency distribution histogram. The frequency minimum value refers to the minimum value corresponding to the frequency maximum value in the same frequency interval in the frequency distribution histogram.

Specifically, a Gaussian kernel density function estimation method is used to perform Gaussian smoothing on the frequency distribution histogram corresponding to the original image, and obtain a Gaussian smooth curve corresponding to the frequency distribution histogram. Based on the frequency maxima and frequency minima on the Gaussian smooth curve, obtain the pixel values on the horizontal axis corresponding to the frequency maxima and frequency minima in order to subsequently based on the obtained frequency maxima and frequency minima Corresponding pixel values are convenient for hierarchical segmentation of the original image to obtain a layered image.

S233: Perform hierarchical segmentation processing on the original image based on the frequency maximum and frequency minimum to obtain a layered image.

The layered image is an image obtained by performing layered segmentation processing on the original image based on the maximum and minimum values. The server first obtains the pixel values corresponding to the maximum frequency value and the minimum frequency value, and processes the original image according to the pixel values corresponding to the maximum frequency value. How many frequency maximum values are in the original image, the corresponding original image The number of pixel values is divided into classes; then the pixel value corresponding to the minimum frequency value is used as the boundary value between the classes, and the original image is layered according to the class and the boundary between the classes to obtain the layering image.

For example, the pixel values corresponding to the frequency maximum in the original image are 11, 53, 95, 116, and 158, and the pixel values corresponding to the minimum frequency are 21, 63, 105, and 135, respectively. According to the number of frequency maxima in the original image, it can be determined that the pixel values of the original image can be divided into 5 categories, the original image can be divided into 5 layers, and the pixel values corresponding to the frequency minima are used as the Boundary value, because the minimum pixel value is 0 and the maximum pixel value is 255. Therefore, according to the boundary value between classes, a layered image with a pixel value of 11 can be determined, and the pixel value corresponding to the layered image is [ 0,21); a layered image with a pixel value of 53 and the corresponding pixel value is [21,63); a layered image with a pixel value of 95 and the corresponding pixel value is [ 63,105); a layered image with a pixel value of 116 and the corresponding pixel value is [105,135); a layered image with a pixel value of 158 and the corresponding layer value is [135,255].

S234: Obtain a target image including handwritten Chinese characters based on the layered image.

After obtaining the layered image, the server performs binarization, erosion, and superposition processing on the layered image to obtain a target image including handwritten Chinese characters. The binarization process refers to a process in which the pixel value of a pixel on a layered image is set to 0 (black) or 1 (white), and the entire layered image presents an obvious black and white effect. After the layered image is binarized, the binarized layered image is corroded to remove the background image part and retain the handwritten Chinese characters on the layered image. Because the pixel values on each layered image are pixel values belonging to different ranges, after the layered image is corroded, each layered image needs to be superimposed to generate a target image containing only handwritten Chinese characters. The superimposing process refers to a process of superimposing a layered image with only a handwritten portion into an image, thereby achieving the purpose of obtaining a target image containing only handwritten Chinese characters. In this embodiment, the layered image is superimposed using the imadd function to obtain a target image containing only handwritten Chinese characters. The imadd function is a function in computer language for superimposing layered images.

In one embodiment, as shown in FIG. 8, in step S234, that is, based on the layered image, obtaining a target image including handwritten Chinese characters, specifically includes the following steps:

S2341: Binarize the layered image to obtain a binarized image.

A binarized image refers to an image obtained by binarizing a sub-image. Specifically, after the server obtains the layered image, it compares the sampled pixel value of the layered image with a preselected threshold, and sets the pixel value greater than or equal to the threshold to 1 and the pixel value less than the threshold to 0. process. The sampled pixel value is the pixel value corresponding to each pixel point in the layered image. The size of the threshold value will affect the effect of the binarization process of the layered image. When the threshold value is selected properly, the effect of the binarization process on the layered image is better; when the threshold value is not selected properly, the effect of the binarization process of the layered image will be affected. effect. To facilitate operations and simplify the calculation process, the threshold in this embodiment is determined by the developer based on experience. Binarize the layered image to facilitate subsequent corrosion treatment.

S2342: Detect pixels in the binarized image to obtain a connected area corresponding to the binarized image.

The connected area refers to an area surrounded by adjacent pixels around a specific pixel. In a binarized image, a connected region means that the neighboring pixels around it are all 0, and a specific pixel and the neighboring pixel are 1, for example, a particular pixel is 0, and the surrounding neighboring pixels are 1, and the neighboring pixels are surrounded. The resulting area is used as the connected area.

Specifically, the binarized image corresponds to a pixel matrix, which includes rows and columns. Detecting pixels in a binarized image specifically includes the following processes: (1) Scan the pixel matrix line by line, group consecutive white pixels in each line into a sequence called a cluster, and note its starting point, End point and line number. (2) For the clique in all rows except the first row, if it does not overlap with any clique in the previous row, give it a new label; if it only overlaps with a clique in the previous row , Assign the label of the group in the previous line to it; if it has a coincident area with more than 2 groups in the previous line, give the current group a minimum label of the associated group, and assign these The tokens in the clique are written into equivalent pairs, indicating that they belong to a class. For example, if there are 2 clusters (1 and 2) in the second row with overlapping areas, then the smallest number given to the 2 clusters in the previous row is 1, and the groups in the previous row are assigned The equivalence pair written by the tag will be recorded as (1, 2) equivalence pair. Equivalent pairs refer to the marks of two cliques connected to each other. For example, (1, 2) indicates that the clique of mark 1 and the clique of mark 2 are connected to each other, which is a connected region. In this embodiment, eight adjacent pixels adjacent to a specific pixel in the pixel matrix are used as the connected region of the element.

S2343: Eroding and superimposing the connected area corresponding to the binary image to obtain a target image including handwritten Chinese characters.

Among them, the etching process is an operation for removing the content of a part of an image in morphology. The built-in imerode function is used to etch the connected areas of the binary image. Specifically, etching the connected region corresponding to the binarized image includes the following steps: First, an n × n structural element is selected. In this embodiment, the value of 8 elements adjacent to each element in the pixel matrix is used as The connected region of this element is, therefore, the selected structural element is a 3 × 3 pixel matrix. The structural element is an n × n pixel matrix, where the matrix elements include 0 or 1. Scan the pixel matrix of the layered binarized image to obtain pixels with a pixel value of 1 and compare whether the 8 adjacent pixels adjacent to the pixel are all 1; if they are all 1, they remain unchanged; if not, If it is 1, the 8 adjacent pixels adjacent to the pixel point in the pixel matrix will become 0 (black). The part that becomes 0 is the part where the layered binarized image is corroded. Matlab is an application software for numerical calculations in the field of mathematical technology applications.

The binarized image is filtered based on the preset anti-corrosion capability range of the hand-written region. Partial deletion of the binary image that is not within the anti-corrosion capability of the hand-written region is obtained to obtain the anti-corrosion capability of the hand-written region in the binary image Within the range. The target pixel image containing only handwritten Chinese characters can be obtained by superimposing the pixel matrix corresponding to each binarized image portion that fits the range of the corrosion resistance of the handwritten area. Among them, the anti-corrosion ability of the hand-written area can adopt the formula:

Calculated, s ₁ represents the total area after being corroded in the binarized image, s ₂ represents the total area before being corroded in the binarized image, and p is the corrosion resistance of the handwritten area.

For example, the preset anti-corrosion range of the handwriting area is [0.01, 0.5], according to the formula

Calculate the ratio p between the total area of each binarized image and the total area before the binarized image. By calculating the ratio p of the total area after erosion to the total area before erosion in the binarized image, which is not in the range of the anti-corrosion capability of the handwritten area, it means that the binarized image of the area is a background image instead of Write by hand and need to be etched to remove the background image. If the ratio p of the total area after erosion to the total area before erosion in the binarized image is in the range of [0.01, 0.5], it means that the binarized image of the region is a handwritten Chinese character and needs to be retained. The pixel matrix corresponding to the retained binary image is superimposed to obtain a target image containing handwritten Chinese characters.

In steps S2341-S2343, the binarized image is binarized to obtain a binarized image, and then pixels in the binarized image are detected and labeled to obtain a connected area corresponding to the binarized image. The elements in the identical pixel matrix all become 0, the binarized image with element 0 is black, and the black part is the corroded part of the binarized image. The total area of the binarized image is calculated by calculating And the ratio of the total area of the binarized image before being eroded, to determine whether the ratio is within the preset anti-corrosion range of the handwriting area, in order to remove the background image in each layered image, retain the handwritten Chinese characters, and finally replace each A layered image is superimposed to achieve the purpose of obtaining the target image.

S24: Single-font cutting is performed on the target image using a vertical projection method to obtain a single-word image to be recognized.

The cutting process of single font cutting of the target image by the vertical projection method is the same as step S12. To avoid repetition, details are not described herein again. The single character image to be recognized is a single font image used for input model recognition.

S25: Input the single character image to be recognized into the target handwriting recognition model for recognition, and obtain a handwritten Chinese character corresponding to each single character image to be recognized.

Among them, the target handwriting recognition model is acquired using a handwriting model training method. Specifically, the server inputs the to-be-recognized word image into the target handwriting recognition model for recognition, so that the target handwriting recognition model can contact the context for recognition, obtain handwritten Chinese characters corresponding to each to-be-recognized word image, and improve recognition accuracy.

In this embodiment, a user may collect an image to be identified containing handwritten Chinese characters and upload it to a server through a collection module on a computer device, so that the server obtains the image to be identified. Then, the server preprocesses the to-be-recognized image and obtains an original image that excludes interference factors. Kernel density estimation algorithm is used to process the original image, remove the background image, and obtain the target image containing only handwritten Chinese characters to further eliminate interference. The vertical projection method is used to cut the single font of the target image to obtain the single character image to be recognized, which is easy to implement. The server inputs the to-be-recognized word image into the target handwriting recognition model based on the long-term and short-term memory neural network for recognition, so that the to-be-recognized word image has timeliness, so that the target handwriting recognition model can contact the context for recognition and obtain each Recognize handwritten Chinese characters corresponding to single-word images, and improve the accuracy of recognition.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

In one embodiment, a handwritten image recognition device is provided. The handwritten image recognition device corresponds to the handwritten image recognition method in the above embodiment in a one-to-one correspondence. As shown in FIG. 9, the handwritten image recognition device includes an image acquisition module 21, an original image acquisition module 22, a target image acquisition module 23, a single character image acquisition module 24, and a handwritten Chinese character acquisition module 25. The detailed description of each function module is as follows:

The to-be-recognized image acquisition module 21 is configured to obtain the to-be-recognized image, where the to-be-recognized image includes a handwritten Chinese character and a background picture.

The original image obtaining module 22 is configured to preprocess the image to be identified to obtain an original image.

A target image acquisition module 23 is configured to process the original image by using a kernel density estimation algorithm, remove a background picture, and obtain a target image including handwritten Chinese characters.

The to-be-recognized single-word image acquisition module 24 is configured to perform single-font cutting on the target image by using a vertical projection method to obtain the to-be-recognized single-word image.

A handwritten Chinese character acquisition module 25 is configured to input an image of a to-be-recognized character into a target handwriting recognition model for recognition, and obtain a handwritten Chinese character corresponding to each image of the to-be-recognized word; wherein the target handwriting recognition model adopts Obtained by the model training method.

Specifically, the original image acquisition module 22 includes a grayscale image acquisition unit 221 and an original image acquisition unit 222.

A grayscale image acquisition unit 221 is configured to perform enlargement and grayscale processing on an original image to obtain a grayscale image.

The original image obtaining unit 222 is configured to perform normalization processing on the grayscale image to obtain the original image. The formula of the normalization processing is:

Specifically, the target image acquisition module 23 includes an original image histogram acquisition unit 231, a frequency extreme value acquisition unit 232, a layered image acquisition unit 233, and a target image acquisition unit 234.

The original image histogram obtaining unit 231 is configured to perform statistics on pixel values in the original image to obtain a histogram of the original image.

A frequency extreme value acquisition unit 232 is configured to process a histogram of the original image by using a Gaussian kernel density estimation algorithm, and obtain at least one frequency maximum value and at least one frequency extreme value acquisition unit corresponding to the histogram of the original image. Small value.

A layered image acquisition unit 233 is configured to perform layered segmentation processing on the original image based on the frequency maximum and frequency minimum to obtain a layered image.

The target image acquisition unit 234 is configured to acquire a target image including a handwritten Chinese character based on the layered image.

Specifically, the target image acquisition unit 234 includes a binarized image acquisition subunit 2341, a connected region acquisition subunit 2342, and a target image acquisition subunit 2343.

A binarized image acquisition subunit 2341 is configured to perform binarization processing on the layered image to obtain a binarized image.

The connected region acquisition subunit 2342 is configured to detect pixels in the binarized image and obtain a connected region corresponding to the binarized image.

A target image acquisition subunit 2343 is configured to perform erosion and superposition processing on the connected areas corresponding to the binary image, and acquire a target image including handwritten Chinese characters.

For specific limitations on the handwritten image recognition device, reference may be made to the foregoing limitations on the handwritten image recognition method, and details are not described herein again. Each module in the above-mentioned handwritten image recognition device may be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the hardware in or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. 10. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in a non-volatile storage medium. The database of the computer device is used to store data generated or obtained during the execution of the handwritten image recognition method, such as handwritten Chinese characters. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by one or more processors, the one or more processors are executed to implement a handwritten image recognition method.

In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor. When the processor executes the computer-readable instructions, the processor implements the following steps: Recognize the image, the image to be recognized includes handwritten Chinese characters and background pictures; preprocess the image to be recognized to obtain the original image; use the kernel density estimation algorithm to process the original image, remove the background image, and obtain the target image including the handwritten Chinese characters; use vertical projection Method to perform single font cutting on the target image to obtain the to-be-recognized word image; input the to-be-recognized word image to the target handwriting recognition model for recognition, and obtain handwritten Chinese characters corresponding to each to-be-recognized word image; of which the target handwriting recognition model It was acquired using handwriting model training methods.

In one embodiment, when the processor executes the computer-readable instructions, the following steps are further implemented: the pixel values in the original image are counted to obtain the original image histogram; the Gaussian kernel density estimation method is used to process the original image histogram to obtain At least one frequency maximum and at least one frequency minimum corresponding to the original image histogram; performing hierarchical segmentation processing on the original image based on the frequency maximum and frequency minimum to obtain a layered image; based on the layered image To get the target image including handwritten Chinese characters.

In an embodiment, when the processor executes the computer-readable instructions, the following steps are further implemented: binarizing the layered image to obtain a binarized image; detecting pixels in the binarized image to obtain a kernel density The estimation algorithm corresponds to the connected area of the binary image; the connected area of the binary image is corroded and superimposed to obtain a target image including handwritten Chinese characters.

In one embodiment, one or more non-volatile readable storage media storing computer-readable instructions are provided, and when the computer-readable instructions are executed by one or more processors, the one or more The processor executes the following steps: obtaining an image to be identified, the image to be identified includes handwritten Chinese characters and a background image; preprocessing the image to be identified to obtain the original image; processing the original image using a kernel density estimation algorithm to remove the background image, Obtain the target image including handwritten Chinese characters; use the vertical projection method to cut the single image of the target image to obtain the image of the single character to be recognized; input the image of the single character to be recognized into the target handwriting recognition model to obtain the corresponding image of each single character The handwritten Chinese character recognition model is obtained by using the handwriting model training method; the target handwriting recognition model is obtained by using the handwriting model training method.

In one embodiment, when the computer-readable instructions are executed by one or more processors, the execution of the one or more processors further implements the following steps: performing statistics on pixel values in the original image to obtain the original Image histogram; Gaussian kernel density estimation method is used to process the original image histogram to obtain at least one frequency maximum and at least one frequency minimum corresponding to the original image histogram; based on the frequency maximum and frequency minimum The original image is subjected to layered segmentation processing to obtain a layered image; based on the layered image, a target image including handwritten Chinese characters is obtained.

In one embodiment, when the computer-readable instructions are executed by one or more processors, the execution of the one or more processors further implements the following steps: binarizing the layered image to obtain two Digitized image; detect and mark the pixels in the binarized image to obtain the connected area corresponding to the kernel density estimation algorithm binarized image; etch and overlay the connected area corresponding to the binarized image to obtain handwritten Chinese characters The target image.

A person of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by using computer-readable instructions to instruct related hardware. The computer-readable instructions can be stored in a non-volatile computer. In the readable storage medium, the computer-readable instructions, when executed, may include the processes of the embodiments of the methods described above. Wherein, any reference to the storage, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile storage. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Those skilled in the art can clearly understand that, for the convenience and brevity of the description, only the above-mentioned division of functional units and modules is used as an example. In practical applications, the above functions can be assigned by different functional units, Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above.

The above-mentioned embodiments are only used to describe the technical solution of the present application, but not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still implement the foregoing implementations. The technical solutions described in the examples are modified, or some of the technical features are equivalently replaced; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of this application.

Claims

A handwriting model training method is characterized in that it includes:

Obtain training handwritten Chinese images;

Performing a single font cutting on the training handwritten Chinese image by using a vertical projection method to obtain a training single font image;

Dividing the training single font image into a training set and a test set according to a preset ratio;

Sequentially label the training single font images in the training set, and input the labeled training single font images into a long-term and short-term memory neural network for training, and use a batch gradient descent algorithm to network parameters of the long-term and short-term memory neural network Update to get the original handwriting recognition model;

The original handwriting recognition model is tested by using the training single font image in the test set, and when the test accuracy is greater than a preset accuracy rate, a target handwriting recognition model is obtained.
The method for training a handwriting model according to claim 1, wherein the labeled training single font image is input into a long-term and short-term memory neural network for training, and a batch gradient descent algorithm is used for the long-term and short-term memory neural network. Update network parameters to obtain the original handwriting recognition model, including:

Using a first activation function to process the training single font image in the hidden layer of the long-term and short-term memory neural network to obtain a neuron carrying an activation state identifier;

Applying a second activation function to the neuron carrying the activation state identifier in the hidden layer of the long-term and short-term memory neural network to obtain the output value of the hidden layer of the long-term and short-term memory neural network;

According to the output value of the hidden layer of the long-term and short-term memory neural network, a batch gradient descent algorithm is used to update the network parameters of the long-term and short-term memory neural network to obtain the target handwriting recognition model.
The method of claim 2, wherein the formula of the batch gradient descent algorithm is:
with
Among them, J (θ) is a loss function, m is the number of single font images trained in the training set, θ j is a network parameter of the long-term and short-term memory neural network in the j-th layer, and h θ (x) is the long-term and short-term The output value of the hidden layer of the memory neural network, (x i , y i ) represents the i-th training single font image.
A handwritten image recognition method, comprising:

Obtaining an image to be identified, where the image to be identified includes handwritten Chinese characters and background pictures;

Preprocessing the image to be identified to obtain an original image;

Processing the original image using a kernel density estimation algorithm, removing the background picture, and obtaining a target image including the handwritten Chinese character;

Performing a single font cutting on the target image using a vertical projection method to obtain a single character image to be recognized;

Inputting the to-be-recognized word image into a target handwriting recognition model for recognition, and obtaining handwritten Chinese characters corresponding to each of the to-be-recognized word images; wherein the target handwriting recognition model adopts any of claims 1-3 It is described in the handwriting model training method.
The method of claim 4, wherein the processing of the original image by using a kernel density estimation algorithm, removing the background image, and obtaining a target image including the handwritten Chinese character comprises:

Performing statistics on pixel values in the original image to obtain a histogram of the original image;

Processing the original image histogram using a Gaussian kernel density estimation method to obtain at least one frequency maximum and at least one frequency minimum corresponding to the original image histogram;

Performing hierarchical segmentation processing on the original image based on the frequency maximum and frequency minimum to obtain a layered image;

Based on the layered image, a target image including the handwritten Chinese character is acquired.
The method for recognizing a handwritten image according to claim 5, wherein the acquiring a target image including the handwritten Chinese character based on the layered image comprises:

Performing a binarization process on the layered image to obtain a binarized image;

Detect and mark pixels in the binarized image to obtain a connected area corresponding to the binarized image;

Eroding and superimposing the connected area corresponding to the binary image to obtain the target image including the handwritten Chinese character.
A handwriting model training device, comprising:

Training handwritten Chinese image acquisition module for acquiring training handwritten Chinese images;

A training handwritten Chinese image division module, configured to divide the trained handwritten Chinese image into a training set and a test set according to a preset ratio;

A training single font image acquisition module, configured to use a vertical projection method to perform single font cutting on the training handwritten Chinese image to obtain a training single font image;

The original handwriting recognition model acquisition module is used to sequentially label the training single font images in the training set, and input the labeled single font images into the long-term and short-term memory neural network for training. The network parameters of the long-term and short-term memory neural network are updated to obtain the original handwriting recognition model;

A target handwriting recognition model acquisition module is configured to test the original handwriting recognition model using a training single font image in the test set, and obtain a target handwriting recognition model when a test accuracy rate is greater than a preset accuracy rate.
A handwritten image recognition device, comprising:

A to-be-recognized image acquisition module, configured to obtain the to-be-recognized image, the to-be-recognized image includes handwritten Chinese characters and a background picture;

An original image acquisition module, configured to preprocess the image to be identified to obtain an original image;

A target image acquisition module, configured to process the original image by using a kernel density estimation algorithm, remove the background picture, and obtain a target image including the handwritten Chinese character;

The to-be-recognized single-word image acquisition module is configured to obtain a to-be-recognized single-word image by using a vertical projection method to perform single-font cutting on the target image;

A handwritten Chinese character acquisition module is configured to input the to-be-recognized word image into a target handwriting recognition model for recognition, and obtain handwritten Chinese characters corresponding to each of the to-be-recognized word images; wherein the target handwriting recognition model adopts the claims Obtained by the handwriting model training method according to any one of 1-3.
A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein when the processor executes the computer-readable instructions, the processor implements Right next steps:

Obtain training handwritten Chinese images;

Performing a single font cutting on the training handwritten Chinese image by using a vertical projection method to obtain a training single font image;

Dividing the training single font image into a training set and a test set according to a preset ratio;

Sequentially label the training single font images in the training set, and input the labeled training single font images into a long-term and short-term memory neural network for training, and use a batch gradient descent algorithm to network parameters of the long-term and short-term memory neural network Update to get the original handwriting recognition model;

The original handwriting recognition model is tested by using the training single font image in the test set, and when the test accuracy is greater than a preset accuracy rate, a target handwriting recognition model is obtained.
The computer device according to claim 9, wherein the labeled training single font image is input to a long-term and short-term memory neural network for training, and a batch gradient descent algorithm is used for the network of the long-term and short-term memory neural network. Parameters are updated to obtain the original handwriting recognition model, including:

Using a first activation function to process the training single font image in the hidden layer of the long-term and short-term memory neural network to obtain a neuron carrying an activation state identifier;

Using a second activation function to process the neuron carrying the activation state identifier at the hidden layer of the long-term and short-term memory neural network to obtain the output value of the hidden layer of the long-term and short-term memory neural network;

According to the output value of the hidden layer of the long-term and short-term memory neural network, a batch gradient descent algorithm is used to update the network parameters of the long-term and short-term memory neural network to obtain the target handwriting recognition model.
The computer device according to claim 10, wherein the formula of the batch gradient descent algorithm is:
with
Among them, J (θ) is a loss function, m is the number of single font images trained in the training set, θ j is a network parameter of the long-term and short-term memory neural network in the j-th layer, and h θ (x) is the long-term and short-term The output value of the hidden layer of the memory neural network, (x i , y i ) represents the i-th training single font image.
A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein when the processor executes the computer-readable instructions, the processor implements Right next steps:

Obtaining an image to be identified, where the image to be identified includes handwritten Chinese characters and background pictures;

Preprocessing the image to be identified to obtain an original image;

Processing the original image using a kernel density estimation algorithm, removing the background picture, and obtaining a target image including the handwritten Chinese character;

Performing a single font cutting on the target image using a vertical projection method to obtain a single character image to be recognized;

Inputting the to-be-recognized word image into a target handwriting recognition model for recognition, and obtaining handwritten Chinese characters corresponding to each of the to-be-recognized word images; wherein the target handwriting recognition model adopts any of claims 1-3 It is described in the handwriting model training method.
The computer device according to claim 12, wherein the processing of the original image by using a kernel density estimation algorithm, removing the background picture, and obtaining a target image including the handwritten Chinese character comprises:

Performing statistics on pixel values in the original image to obtain a histogram of the original image;

Processing the original image histogram using a Gaussian kernel density estimation method to obtain at least one frequency maximum and at least one frequency minimum corresponding to the original image histogram;

Performing hierarchical segmentation processing on the original image based on the frequency maximum and frequency minimum to obtain a layered image;

Based on the layered image, a target image including the handwritten Chinese character is acquired.
The computer device according to claim 13, wherein the acquiring a target image including the handwritten Chinese character based on the layered image comprises:

Performing a binarization process on the layered image to obtain a binarized image;

Detect and mark pixels in the binarized image to obtain a connected area corresponding to the binarized image;

Eroding and superimposing the connected area corresponding to the binary image to obtain the target image including the handwritten Chinese character.
One or more non-volatile readable storage media storing computer readable instructions, characterized in that when the computer readable instructions are executed by one or more processors, the one or more processors are caused to execute The following steps:

Obtain training handwritten Chinese images;

Performing a single font cutting on the training handwritten Chinese image by using a vertical projection method to obtain a training single font image;

Dividing the training single font image into a training set and a test set according to a preset ratio;

Sequentially label the training single font images in the training set, and input the labeled training single font images into a long-term and short-term memory neural network for training, and use a batch gradient descent algorithm to network parameters of the long-term and short-term memory neural network Update to get the original handwriting recognition model;

The original handwriting recognition model is tested by using the training single font image in the test set, and when the test accuracy is greater than a preset accuracy rate, a target handwriting recognition model is obtained.
The non-volatile readable storage medium according to claim 15, wherein the labeled training single font image is input into a long-term and short-term memory neural network for training, and a batch gradient descent algorithm is used for the length and length. The network parameters of the memory neural network are updated to obtain the original handwriting recognition model, including:

Using a first activation function to process the training single font image in the hidden layer of the long-term and short-term memory neural network to obtain a neuron carrying an activation state identifier;

Applying a second activation function to the neuron carrying the activation state identifier in the hidden layer of the long-term and short-term memory neural network to obtain the output value of the hidden layer of the long-term and short-term memory neural network;

According to the output value of the hidden layer of the long-term and short-term memory neural network, a batch gradient descent algorithm is used to update the network parameters of the long-term and short-term memory neural network to obtain the target handwriting recognition model.
The non-volatile readable storage medium of claim 16, wherein the formula of the batch gradient descent algorithm is:
with
Among them, J (θ) is a loss function, m is the number of single font images trained in the training set, θ j is a network parameter of the long-term and short-term memory neural network in the j-th layer, and h θ (x) is the long-term and short-term The output value of the hidden layer of the memory neural network, (x i , y i ) represents the i-th training single font image.
One or more non-volatile readable storage media storing computer readable instructions, characterized in that when the computer readable instructions are executed by one or more processors, the one or more processors are caused to execute The following steps:

Obtaining an image to be identified, where the image to be identified includes handwritten Chinese characters and background pictures;

Preprocessing the image to be identified to obtain an original image;

Processing the original image using a kernel density estimation algorithm, removing the background picture, and obtaining a target image including the handwritten Chinese character;

Performing a single font cutting on the target image using a vertical projection method to obtain a single character image to be recognized;

Inputting the to-be-recognized word image into a target handwriting recognition model for recognition, and obtaining handwritten Chinese characters corresponding to each of the to-be-recognized word images; wherein the target handwriting recognition model adopts any of claims 1-3 It is described in the handwriting model training method.
The non-volatile readable storage medium according to claim 18, wherein the original image is processed by using a kernel density estimation algorithm, the background picture is removed, and a target image including the handwritten Chinese character is obtained ,include:

Performing statistics on pixel values in the original image to obtain a histogram of the original image;

Processing the original image histogram using a Gaussian kernel density estimation method to obtain at least one frequency maximum and at least one frequency minimum corresponding to the original image histogram;

Performing hierarchical segmentation processing on the original image based on the frequency maximum and frequency minimum to obtain a layered image;

Based on the layered image, a target image including the handwritten Chinese character is acquired.
The non-volatile readable storage medium according to claim 19, wherein the acquiring a target image including the handwritten Chinese character based on the layered image comprises:

Performing a binarization process on the layered image to obtain a binarized image;

Detect and mark pixels in the binarized image to obtain a connected area corresponding to the binarized image;

Corrosion and superposition processing are performed on the connected areas corresponding to the binarized image to obtain a target image including handwritten Chinese characters.