WO2021212652A1

WO2021212652A1 - Handwritten english text recognition method and device, electronic apparatus, and storage medium

Info

Publication number: WO2021212652A1
Application number: PCT/CN2020/098237
Authority: WO
Inventors: 赵振兴
Original assignee: 平安国际智慧城市科技股份有限公司
Priority date: 2020-04-23
Filing date: 2020-06-24
Publication date: 2021-10-28
Also published as: CN111639527A

Abstract

A handwritten English text recognition method and device, an electronic apparatus, and a storage medium, relating to the technical field of artificial intelligence. The method comprises: an electronic apparatus acquiring a handwritten English text line image set (S11); the electronic apparatus performing uniform scaling on all images in the handwritten English text line image set according to a preconfigured width threshold so as to obtain multiple scaled images (S12); the electronic apparatus determining, from the multiple scaled images, a first standard image and an image having a length to be extended (S13); the electronic apparatus adding, according to a preconfigured length threshold, a blank region to the image having the length to be extended so as to obtain a second standard image (S14); the electronic apparatus randomly adjusting the first standard image and the second standard image so as to obtain a training image (S15); the electronic apparatus training an initial recognition model according to a backpropagation algorithm and the training image so as to obtain a trained recognition model (S16); the electronic apparatus acquiring an image to undergo recognition (S17); and the electronic apparatus inputting the image to undergo recognition into the trained recognition model, and acquiring a recognition result (S18). The method allows recognition to be performed on an entire line of English text.

Description

English handwritten text recognition method, device, electronic equipment and storage medium

This application claims to be submitted to the Chinese Patent Office on April 23, 2020. The application number is 202010329360.1. The title of the invention is "English handwritten text recognition method, device, electronic equipment and storage medium". The priority of the Chinese patent application, the entire content is by reference Incorporated in this application.

Technical field

This application relates to the field of artificial intelligence technology, and in particular to an English handwritten text recognition method, device, electronic equipment, and storage medium.

Background technique

At present, artificial intelligence can be used to identify text in text images, such as English letters and individual words in text images, but the inventor realizes that some text in text images is handwritten by users, which are written out due to different personal writing habits. The text is different in shape, and for the entire line of text, there are spaces between words, with punctuation marks, and the text length is not fixed, resulting in the entire line of English text cannot be recognized.

Therefore, how to recognize the entire line of English text is a technical problem that needs to be solved urgently.

Summary of the invention

In view of the above, it is necessary to provide an English handwritten text recognition method, device, electronic equipment, and storage medium, which can recognize the entire line of English text.

The first aspect of this application provides a method for recognizing English handwritten text. The method includes:

Acquiring a picture collection of English handwritten text lines, wherein the pictures in the picture collection of English handwritten text lines include English letters, spaces, and punctuation marks;

According to a preset width threshold, all pictures in the set of pictures of the English handwritten text line are scaled equally to obtain multiple scaled pictures;

From the plurality of zoomed pictures, determine a first standard picture and a picture with a length to be supplemented, wherein the length of the first standard picture is equal to a preset length threshold, and the length of the picture with a length to be supplemented is less than the preset length Threshold

Adding a blank area to the to-be-added length picture according to the preset length threshold to obtain a second standard picture, wherein the length of the second standard picture is equal to the preset length threshold;

Randomly adjust the first standard picture and the second standard picture to obtain a training picture, wherein the randomly adjusted objects include picture brightness, picture contrast, picture saturation, noise, and picture font size;

Training the initial recognition model according to the backpropagation algorithm and the training pictures to obtain a trained recognition model;

Obtain the picture to be recognized;

The picture to be recognized is input into the trained recognition model to obtain a recognition result, where the recognition result includes English, spaces, and punctuation in the picture to be recognized.

A second aspect of the present application provides an electronic device including a processor and a memory, and the processor is configured to execute computer-readable instructions stored in the memory to implement the following steps:

Obtain the picture to be recognized;

The picture to be recognized is input into the trained recognition model to obtain a recognition result, where the recognition result includes English letters, spaces, and punctuation in the picture to be recognized.

A third aspect of the present application provides a computer-readable storage medium having at least one computer-readable instruction stored thereon, and the at least one computer-readable instruction is executed by a processor to implement the following steps:

Obtain the picture to be recognized;

A fourth aspect of the present application provides an English handwritten text recognition device, the device includes:

An acquiring module for acquiring a picture collection of English handwritten text lines, wherein the pictures of the picture set of English handwritten text lines include English letters, spaces, and punctuation marks;

The zoom module is used to scale all the pictures in the English handwritten text line picture set in equal proportions according to a preset width threshold to obtain multiple zoom pictures;

The determining module is configured to determine a first standard picture and a picture with a length to be supplemented from the plurality of zoom pictures, wherein the length of the first standard picture is equal to a preset length threshold, and the length of the picture with a length to be supplemented is less than The preset length threshold;

An adding module, configured to add a blank area to the to-be-compensated length picture according to the preset length threshold to obtain a second standard picture, wherein the length of the second standard picture is equal to the preset length threshold;

The adjustment module is used to randomly adjust the first standard picture and the second standard picture to obtain a training picture, wherein the randomly adjusted objects include picture brightness, picture contrast, picture saturation, noise, and picture fonts size;

The training module is used to train the initial recognition model according to the backpropagation algorithm and the training picture to obtain a trained recognition model;

The obtaining module is also used to obtain the picture to be recognized;

The input module is used to input the picture to be recognized into the trained recognition model to obtain a recognition result, where the recognition result includes English, spaces, and punctuation in the picture to be recognized.

Based on the above technical solutions, in this application, a recognition model can be trained to recognize the entire line of English text by using a large set of pictures of English handwritten text lines. Deformation occurs, and the brightness, contrast, saturation, and noise of the picture are randomly adjusted to simulate the types of pictures generated in different scenes, which can improve the accuracy of the recognition model and can recognize English text lines in various pictures. At the same time, after the images for training are scaled proportionally, the insufficient length of the images is supplemented to ensure that all the images have the same length and width, so that a large number of images can be used for training at the same time, which improves the speed of training the recognition model.

Description of the drawings

Fig. 1 is a flowchart of a preferred embodiment of a method for recognizing English handwritten text disclosed in the present application.

Fig. 2 is a functional block diagram of a preferred embodiment of an English handwritten text recognition device disclosed in the present application.

FIG. 3 is a schematic structural diagram of an electronic device implementing a preferred embodiment of the method for recognizing English handwritten text according to the present application.

Detailed ways

The English handwritten text recognition method of the embodiment of the present application is applied to an electronic device, and can also be applied to a hardware environment composed of an electronic device and a server connected to the electronic device through a network, and is executed by the server and the electronic device. Networks include, but are not limited to: wide area networks, metropolitan area networks, or local area networks.

Please refer to FIG. 1. FIG. 1 is a flowchart of a preferred embodiment of an English handwritten text recognition method disclosed in the present application. Among them, according to different needs, the order of the steps in the flowchart can be changed, and some steps can be omitted.

S11. The electronic device obtains an English handwritten text line picture collection, where the pictures in the English handwritten text line picture collection include English letters, spaces, and punctuation marks.

Among them, the English handwritten text line picture collection can be obtained from the public IAM Handwriting Database (IAM Handwriting Database). The IAM handwriting database contains unlimited English handwritten texts. These English handwritten texts are scanned at a resolution of 300dpi, and Save as a 256 grayscale PNG image.

S12. The electronic device performs equal scaling on all pictures in the set of pictures of the English handwritten text line according to the preset width threshold to obtain multiple scaled pictures.

Wherein, the width of the zoomed picture is a preset width, and the length of the zoomed picture may be different.

In the embodiment of the present application, the proportional zoom can prevent the English letters in the picture from being deformed. The picture can be scaled to a picture with the same width as the preset width, because the aspect ratio of the picture is fixed. If the original aspect ratio of each picture is inconsistent, the width of the scaled picture is the same, but the length is inconsistent.

S13. The electronic device determines a first standard picture and a picture with a length to be supplemented from the multiple zoom pictures, wherein the length of the first standard picture is equal to a preset length threshold, and the length of the picture with a length to be supplemented is less than all The preset length threshold.

In this embodiment of the application, pictures with a length greater than a preset length can be deleted.

S14. The electronic device adds a blank area to the to-be-compensated length picture according to the preset length threshold to obtain a second standard picture, wherein the length of the second standard picture is equal to the preset length threshold.

In the embodiment of the present application, a blank area is added at the left or right end of the to-be-compensated length picture to obtain the second standard picture, so that the size of the picture is consistent. Because the neural network used in training has certain requirements for the input picture (length and width), and pictures that meet the requirements, the picture length and the picture width are the same can be input into the neural network at the same time for training, saving training time.

S15. The electronic device randomly adjusts the first standard picture and the second standard picture to obtain a training picture, wherein the randomly adjusted objects include picture brightness, picture contrast, picture saturation, noise, and picture font size .

In the embodiment of the application, the brightness, contrast, saturation, noise, and font size of the picture can be adjusted to simulate English text pictures taken in different environments, which can increase the diversity of training samples and improve the training effect.

Specifically, the random adjustment of the first standard picture and the second standard picture to obtain the training picture includes:

Obtain the preset zoom multiple interval;

Performing random scaling on the first standard image and the second standard image in equal proportions according to the preset scaling factor interval to obtain a randomly scaled picture;

Mapping the randomly zoomed picture on a canvas of a preset size to obtain a target picture with a consistent size;

Randomly adjust brightness, contrast, and saturation of the target picture, respectively, to obtain pictures with random brightness, random contrast, and random saturation;

Random noise is added to the pictures with random brightness, random contrast, and random saturation to obtain training pictures.

Wherein, the preset zoom factor interval may be [0.6, 1.0], to ensure that the length of the zoomed picture will not exceed the original length and the width will not exceed the original width (that is, the zoomed picture will not). Map on the canvas of preset size.

In this optional implementation manner, the zoom factor can be randomly obtained from the preset zoom factor interval to zoom the picture, simulating the situation where there is a font size difference in the writing of different people. The purpose of randomly adjusting the brightness, contrast, and saturation of the picture is to simulate pictures with different effects in real scenes due to different picture backgrounds and different shooting light. Random noise is added to simulate pictures of different quality. Through randomly adjusted training images, a recognition model with higher accuracy and wider applicability can be trained.

S16. The electronic device trains the initial recognition model according to the backpropagation algorithm and the training picture to obtain a trained recognition model.

Among them, the neural network in the initial recognition model can have a loss function. The loss function is used to calculate the distance between the data output by the current neural network modeling and the ideal data. The back propagation algorithm can update each parameter in the neural network. , So that the loss value calculated by the loss function is continuously reduced, even if the data output by the neural network modeling is constantly close to the ideal data.

Wherein, the initial recognition model includes a convolutional layer, a recurrent layer, and a transcription layer.

Among them, the convolutional layer may be CNN (Convolutional Neural Networks), the recurrent layer may be RNN (Recurrent Neural Network), and the transcription layer may be CTC (Connectionist Temporal Classification).

Specifically, the training of the initial recognition model according to the backpropagation algorithm and the training pictures, and obtaining the trained recognition model includes:

Input the training picture into the convolutional layer of the initial recognition model to obtain image pixel features;

Input the image pixel features into the recurring layer of the initial recognition model to obtain image time series features;

Inputting the image timing features into the transcription layer of the initial recognition model to obtain a tag sequence;

Using a loss function to calculate the loss value corresponding to the tag sequence;

According to the back propagation algorithm and the loss value, the network parameters of the initial recognition model are updated to obtain a trained recognition model.

Wherein, the tag sequence is recognized English text, including English letters, punctuation marks, and spaces.

In this alternative embodiment, the pixel features of the picture can be extracted through the convolutional layer; then the pixel features are input into the recurring layer to obtain the image timing features, and finally the transcription layer can map the image timing features to a label sequence, such as: There are English letters "ab" in the input picture, the obtained image sequence feature can be a set of vectors (t1, t2, t3, t4, t5), and the tag sequence output by the final transcription layer can be "ab".

As an optional implementation manner, the updating the network parameters of the initial recognition model according to the backpropagation algorithm and the loss value to obtain a trained recognition model includes:

According to the back propagation algorithm and the loss value, adjusting the network parameters of the initial recognition model to minimize the loss value to obtain the model to be tested;

Get the preset test set;

Use the test set to test the model to be tested, and determine the accuracy rate at which the model to be tested passes the test;

If the accuracy rate is greater than the preset accuracy rate threshold, it is determined that the model to be tested is a trained recognition model.

Wherein, the test set may be some English text pictures used for testing.

In this alternative embodiment, when the backpropagation algorithm is used to continuously update the parameters of the model, the model can be tested using the test set to obtain the recognition accuracy of the model. If the recognition accuracy of the model meets the preset requirements ( That is, the recognition accuracy is greater than the preset accuracy threshold), it can be considered that the model and training are completed.

As an optional implementation manner, the method further includes:

If the accuracy rate is less than or equal to the preset accuracy rate threshold, determining that the model to be tested is an untrained recognition model;

Retrain the untrained recognition model.

In this optional embodiment, if the recognition accuracy of the model is less than or equal to the preset accuracy threshold, it indicates that the recognition effect of the model has not yet reached the expected recognition effect, and training can be continued or retraining can also be performed.

S17. The electronic device obtains the picture to be recognized.

Wherein, the picture to be recognized may be a picture carrying English letters.

S18. The electronic device inputs the picture to be recognized into the trained recognition model to obtain a recognition result, where the recognition result includes English letters, spaces, and punctuation in the picture to be recognized.

In the embodiment of the present application, the trained recognition model can recognize the entire line of English text in the picture.

As an optional implementation manner, the initial recognition model is trained according to the backpropagation algorithm and the training pictures, and after obtaining the trained recognition model, the method further includes:

Performing tilt correction on the picture to be recognized according to the Hough transform algorithm to obtain a corrected picture;

The inputting the picture to be recognized into the trained recognition model to obtain a recognition result includes:

The correction picture is input into the trained recognition model to obtain a recognition result.

In this optional implementation, the Hough transform can map the letter image to the parameter space, calculate the tilt angle of the letter image, and then rotate the letter image according to the tilt angle of the letter image to obtain Horizontal letter image. It can prevent the problem of poor recognition effect due to the tilt of the letter image caused by personal writing or shooting.

In the method flow described in Figure 1, the recognition model can be trained to recognize the entire line of English text by using a large number of English handwritten text line picture sets. Among them, the training pictures are scaled in equal proportions to ensure the text in the pictures No deformation occurs, and the brightness, contrast, saturation, and noise of the picture are randomly adjusted to simulate the types of pictures generated in different scenes, which can improve the accuracy of the recognition model and can recognize English text lines in various pictures. At the same time, after the images for training are scaled proportionally, the insufficient length of the images is supplemented to ensure that all the images have the same length and width, so that a large number of images can be used for training at the same time, which improves the speed of training the recognition model.

The above are only specific implementations of this application, but the scope of protection of this application is not limited to this. For those of ordinary skill in the art, without departing from the creative concept of this application, they can also make Improvements, but these all belong to the scope of protection of this application.

Please refer to FIG. 2. FIG. 2 is a functional module diagram of a preferred embodiment of an English handwritten text recognition device disclosed in the present application.

In some embodiments, the English handwritten text recognition device runs in an electronic device. The English handwritten text recognition device may include multiple functional modules composed of program code segments, and the program is a series of computer-readable instruction codes. The program code of each program segment in the English handwritten text recognition device can be stored in a memory and executed by at least one processor to perform part or all of the steps in the English handwritten text recognition method described in FIG. 1, specifically Reference may be made to the related description in the method described in FIG. 1, which will not be repeated here.

In this embodiment, the device for recognizing English handwritten text can be divided into multiple functional modules according to the functions it performs. The functional modules may include: an acquisition module 201, a zoom module 202, a determination module 203, an addition module 204, an adjustment module 205, a training module 206, and an input module 207. The module referred to in this application refers to a series of computer-readable instruction segments that can be executed by at least one processor and can complete fixed functions, and are stored in a memory.

The acquiring module 201 is configured to acquire a picture collection of English handwritten text lines, wherein the pictures of the picture set of English handwritten text lines include English letters, spaces, and punctuation marks.

Among them, the English handwritten text line picture collection can be obtained from the public IAM Handwriting Database (IAM Handwriting Database), which contains unlimited English handwritten texts, and these English handwritten texts are scanned at a resolution of 300dpi, and Save as a 256 grayscale PNG image.

The zoom module 202 is configured to perform equal-scale zooming of all the pictures in the set of pictures of the English handwritten text line according to a preset width threshold to obtain multiple zoomed pictures.

The determining module 203 is configured to determine a first standard picture and a to-be-added length picture from the multiple zoomed pictures, wherein the length of the first standard picture is equal to a preset length threshold, and the length of the to-be-added length picture Less than the preset length threshold.

The adding module 204 is configured to add a blank area to the to-be-compensated length picture according to the preset length threshold to obtain a second standard picture, wherein the length of the second standard picture is equal to the preset length threshold.

The adjustment module 205 is configured to perform random adjustments on the first standard picture and the second standard picture to obtain a training picture, wherein the randomly adjusted objects include picture brightness, picture contrast, picture saturation, noise, and picture font size.

The training module 206 is used to train the initial recognition model according to the backpropagation algorithm and the training picture to obtain a trained recognition model.

The obtaining module 201 is also used to obtain a picture to be recognized;

The input module 207 is configured to input the picture to be recognized into the trained recognition model to obtain a recognition result, where the recognition result includes English letters, spaces, and punctuation marks in the picture to be recognized.

As an optional implementation manner, the adjustment module 205 performs random adjustment on the first standard picture and the second standard picture, and the specific method for obtaining the training picture is:

Obtain the preset zoom multiple interval;

As an optional implementation manner, the training module 206 trains the initial recognition model according to the backpropagation algorithm and the training pictures, and the specific method for obtaining the trained recognition model is as follows:

As an optional implementation manner, the training module 206 updates the network parameters of the initial recognition model according to the backpropagation algorithm and the loss value, and the specific method for obtaining the trained recognition model is as follows:

Get the preset test set;

Wherein, the test set may be some English text pictures used for testing.

As an optional implementation manner, the determining module 203 is further configured to determine that the model to be tested is an untrained recognition model if the accuracy rate is less than or equal to a preset accuracy rate threshold;

The training module 206 is also used to retrain the untrained recognition model.

As an optional implementation manner, the device for recognizing English handwritten text may further include:

The correction module is used to train the initial recognition model according to the backpropagation algorithm and the training picture, and after obtaining the trained recognition model, perform tilt correction on the picture to be recognized according to the Hough transform algorithm to obtain Correct the picture.

The input module 207 inputs the image to be recognized into the trained recognition model, and the specific method for obtaining the recognition result is as follows:

In the English handwritten text recognition device described in Figure 2, the recognition model can be trained by using a large number of English handwritten text line picture sets to recognize the entire line of English text. Among them, the training pictures are scaled in equal proportions to ensure that the pictures The text in the text is not deformed, and the brightness, contrast, saturation, and noise of the picture are randomly adjusted to simulate the types of pictures generated in different scenes, which can improve the accuracy of the recognition model, and can recognize English text lines in various pictures . At the same time, after the images for training are scaled proportionally, the insufficient length of the images is supplemented to ensure that all the images have the same length and width, so that a large number of images can be used for training at the same time, which improves the speed of training the recognition model.

As shown in FIG. 3, FIG. 3 is a schematic structural diagram of an electronic device implementing a preferred embodiment of the method for recognizing English handwritten text according to the present application. The electronic device 3 includes a memory 31, at least one processor 32, computer readable instructions 33 stored in the memory 31 and executable on the at least one processor 32, and at least one communication bus 34.

Those skilled in the art can understand that the schematic diagram shown in FIG. 3 is only an example of the electronic device 3, and does not constitute a limitation on the electronic device 3. It may include more or less components than those shown in the figure, or a combination. Certain components, or different components, for example, the electronic device 3 may also include input and output devices, network access devices, and so on.

The electronic device 3 also includes, but is not limited to, any electronic product that can interact with the user through a keyboard, a mouse, a remote control, a touch panel, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, etc. Personal digital assistants (Personal Digital Assistant, PDA), game consoles, interactive network television (Internet Protocol Television, IPTV), smart wearable devices, etc.

The at least one processor 32 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processors, DSPs), and application specific integrated circuits (ASICs). ), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 32 can be a microprocessor, or the processor 32 can also be any conventional processor, etc. The processor 32 is the control center of the electronic device 3, and connects the entire electronic device 3 through various interfaces and lines. Parts.

The memory 31 may be used to store the computer-readable instructions 33 and/or modules/units, and the processor 32 can run or execute the computer-readable instructions and/or modules/units stored in the memory 31, and The data stored in the memory 31 is called to realize various functions of the electronic device 3. The memory 31 may mainly include a storage program area and a storage data area. The storage program area may store an operating system, an application program required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; the storage data area may Data and the like created in accordance with the use of the electronic device 3 are stored. In addition, the memory 31 may include volatile memory such as high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), and a secure digital ( Secure Digital, SD card, Flash Card, at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device.

With reference to FIG. 1, the memory 31 in the electronic device 3 stores a plurality of instructions to implement an English handwritten text recognition method, and the processor 32 can execute the plurality of instructions to achieve:

Obtain the picture to be recognized;

Specifically, for the specific implementation method of the above-mentioned instructions by the processor 32, reference may be made to the description of the relevant steps in the embodiment corresponding to FIG. 1, which will not be repeated here.

In the electronic device 3 described in Figure 3, a recognition model can be trained by using a large set of pictures of English handwritten text lines to recognize the entire line of English text. The text is not deformed, and the brightness, contrast, saturation, and noise of the picture are randomly adjusted to simulate the types of pictures generated in different scenes, which can improve the accuracy of the recognition model, and can recognize English text lines in various pictures. At the same time, after the images for training are scaled proportionally, the insufficient length of the images is supplemented to ensure that all the images have the same length and width, so that a large number of images can be used for training at the same time, which improves the speed of training the recognition model.

If the integrated module/unit of the electronic device 3 is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium, which may be non-easy. A volatile storage medium can also be a volatile storage medium. Based on this understanding, this application implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the computer-readable instructions are executed by the processor, they can implement the steps of the foregoing method embodiments. Wherein, the computer-readable instruction includes computer-readable instruction code, and the computer-readable instruction code may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer-readable instruction code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory).

In the several embodiments provided in this application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional modules.

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any associated diagram marks in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claims can also be implemented by one unit or device through software or hardware. The second class words are used to indicate names, and do not indicate any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present application.

Claims

An English handwritten text recognition method, wherein the English handwritten text recognition method includes:

Acquiring a picture collection of English handwritten text lines, wherein the pictures in the picture collection of English handwritten text lines include English letters, spaces, and punctuation marks;

According to a preset width threshold, all pictures in the set of pictures of the English handwritten text line are scaled equally to obtain multiple scaled pictures;

From the plurality of zoomed pictures, determine a first standard picture and a picture with a length to be supplemented, wherein the length of the first standard picture is equal to a preset length threshold, and the length of the picture with a length to be supplemented is less than the preset length Threshold

Adding a blank area to the to-be-added length picture according to the preset length threshold to obtain a second standard picture, wherein the length of the second standard picture is equal to the preset length threshold;

Randomly adjust the first standard picture and the second standard picture to obtain a training picture, wherein the randomly adjusted objects include picture brightness, picture contrast, picture saturation, noise, and picture font size;

Training the initial recognition model according to the backpropagation algorithm and the training pictures to obtain a trained recognition model;

Obtain the picture to be recognized;

The picture to be recognized is input into the trained recognition model to obtain a recognition result, where the recognition result includes English letters, spaces, and punctuation in the picture to be recognized.
The method for recognizing English handwritten text according to claim 1, wherein said randomly adjusting said first standard picture and said second standard picture to obtain training pictures comprises:

Obtain the preset zoom multiple interval;

Performing random scaling on the first standard image and the second standard image in equal proportions according to the preset scaling factor interval to obtain a randomly scaled picture;

Mapping the randomly zoomed picture on a canvas of a preset size to obtain a target picture with a consistent size;

Randomly adjust brightness, contrast, and saturation of the target picture, respectively, to obtain pictures with random brightness, random contrast, and random saturation;

Random noise is added to the pictures with random brightness, random contrast, and random saturation to obtain training pictures.
The method for recognizing English handwritten text according to claim 1, wherein the training of the initial recognition model according to the backpropagation algorithm and the training picture to obtain the trained recognition model comprises:

Input the training picture into the convolutional layer of the initial recognition model to obtain image pixel features;

Input the image pixel features into the recurring layer of the initial recognition model to obtain image time series features;

Inputting the image timing features into the transcription layer of the initial recognition model to obtain a tag sequence;

Using a loss function to calculate the loss value corresponding to the tag sequence;

According to the back propagation algorithm and the loss value, the network parameters of the initial recognition model are updated to obtain a trained recognition model.
4. The method for recognizing English handwritten text according to claim 3, wherein the updating the network parameters of the initial recognition model according to the backpropagation algorithm and the loss value to obtain the trained recognition model comprises:

According to the back propagation algorithm and the loss value, adjusting the network parameters of the initial recognition model to minimize the loss value to obtain the model to be tested;

Get the preset test set;

Use the test set to test the model to be tested, and determine the accuracy rate at which the model to be tested passes the test;

If the accuracy rate is greater than the preset accuracy rate threshold, it is determined that the model to be tested is a trained recognition model.
4. The method for recognizing English handwritten text according to claim 4, wherein the method for recognizing English handwritten text further comprises:

If the accuracy rate is less than or equal to the preset accuracy rate threshold, determining that the model to be tested is an untrained recognition model;

Retrain the untrained recognition model.
The method for recognizing English handwritten text according to claim 1, wherein the initial recognition model is trained according to the backpropagation algorithm and the training picture, and after a trained recognition model is obtained, the method for recognizing English handwritten text Also includes:

Performing tilt correction on the picture to be recognized according to the Hough transform algorithm to obtain a corrected picture;

The inputting the picture to be recognized into the trained recognition model to obtain a recognition result includes:

The correction picture is input into the trained recognition model to obtain a recognition result.
The method for recognizing English handwritten text according to any one of claims 1 to 6, wherein the initial recognition model includes a convolutional layer, a recurrent layer, and a transcription layer.
An electronic device, wherein the electronic device includes a processor and a memory, and the processor is configured to execute at least one computer-readable instruction stored in the memory to implement the following steps:

Acquiring a picture collection of English handwritten text lines, wherein the pictures in the picture collection of English handwritten text lines include English letters, spaces, and punctuation marks;

According to a preset width threshold, all pictures in the set of pictures of the English handwritten text line are scaled equally to obtain multiple scaled pictures;

From the plurality of zoomed pictures, determine a first standard picture and a picture with a length to be supplemented, wherein the length of the first standard picture is equal to a preset length threshold, and the length of the picture with a length to be supplemented is less than the preset length Threshold

Adding a blank area to the to-be-added length picture according to the preset length threshold to obtain a second standard picture, wherein the length of the second standard picture is equal to the preset length threshold;

Randomly adjust the first standard picture and the second standard picture to obtain a training picture, wherein the randomly adjusted objects include picture brightness, picture contrast, picture saturation, noise, and picture font size;

Training the initial recognition model according to the backpropagation algorithm and the training pictures to obtain a trained recognition model;

Obtain the picture to be recognized;

The picture to be recognized is input into the trained recognition model to obtain a recognition result, where the recognition result includes English letters, spaces, and punctuation in the picture to be recognized.
8. The electronic device according to claim 8, wherein, when the first standard picture and the second standard picture are randomly adjusted to obtain a training picture, the processor executes the at least one computer readable Instructions to achieve the following steps:

Obtain the preset zoom multiple interval;

Performing random scaling on the first standard image and the second standard image in equal proportions according to the preset scaling factor interval to obtain a randomly scaled picture;

Mapping the randomly zoomed picture on a canvas of a preset size to obtain a target picture with a consistent size;

Randomly adjust brightness, contrast, and saturation of the target picture, respectively, to obtain pictures with random brightness, random contrast, and random saturation;

Random noise is added to the pictures with random brightness, random contrast, and random saturation to obtain training pictures.
The electronic device according to claim 8, wherein, when the initial recognition model is trained according to the backpropagation algorithm and the training picture to obtain a trained recognition model, the processor executes the at least one Computer readable instructions to achieve the following steps:

Input the training picture into the convolutional layer of the initial recognition model to obtain image pixel features;

Input the image pixel features into the recurring layer of the initial recognition model to obtain image time series features;

Inputting the image timing features into the transcription layer of the initial recognition model to obtain a tag sequence;

Using a loss function to calculate the loss value corresponding to the tag sequence;

According to the back propagation algorithm and the loss value, the network parameters of the initial recognition model are updated to obtain a trained recognition model.
The electronic device according to claim 10, wherein, when the network parameters of the initial recognition model are updated according to the backpropagation algorithm and the loss value to obtain a trained recognition model, the processor executes all At least one computer-readable instruction is described to implement the following steps:

According to the back propagation algorithm and the loss value, adjusting the network parameters of the initial recognition model to minimize the loss value to obtain the model to be tested;

Get the preset test set;

Use the test set to test the model to be tested, and determine the accuracy rate at which the model to be tested passes the test;

If the accuracy rate is greater than the preset accuracy rate threshold, it is determined that the model to be tested is a trained recognition model.
The electronic device of claim 11, wherein the processor executes the at least one computer-readable instruction to implement the following steps:

If the accuracy rate is less than or equal to the preset accuracy rate threshold, determining that the model to be tested is an untrained recognition model;

Retrain the untrained recognition model.
8. The electronic device according to claim 8, wherein, after the initial recognition model is trained according to the backpropagation algorithm and the training picture to obtain a trained recognition model, the processor executes the at least one Computer readable instructions to achieve the following steps:

Performing tilt correction on the picture to be recognized according to the Hough transform algorithm to obtain a corrected picture;

The inputting the picture to be recognized into the trained recognition model to obtain a recognition result includes:

The correction picture is input into the trained recognition model to obtain a recognition result.
A computer-readable storage medium, wherein the computer-readable storage medium stores at least one computer-readable instruction, and when the at least one computer-readable instruction is executed by a processor, the following steps are implemented:

Acquiring a picture collection of English handwritten text lines, wherein the pictures in the picture collection of English handwritten text lines include English letters, spaces, and punctuation marks;

According to a preset width threshold, all pictures in the set of pictures of the English handwritten text line are scaled equally to obtain multiple scaled pictures;

From the plurality of zoomed pictures, determine a first standard picture and a picture with a length to be supplemented, wherein the length of the first standard picture is equal to a preset length threshold, and the length of the picture with a length to be supplemented is less than the preset length Threshold

Adding a blank area to the to-be-added length picture according to the preset length threshold to obtain a second standard picture, wherein the length of the second standard picture is equal to the preset length threshold;

Randomly adjust the first standard picture and the second standard picture to obtain a training picture, wherein the randomly adjusted objects include picture brightness, picture contrast, picture saturation, noise, and picture font size;

Training the initial recognition model according to the backpropagation algorithm and the training pictures to obtain a trained recognition model;

Obtain the picture to be recognized;

The picture to be recognized is input into the trained recognition model to obtain a recognition result, where the recognition result includes English letters, spaces, and punctuation in the picture to be recognized.
14. The storage medium according to claim 14, wherein when the first standard picture and the second standard picture are randomly adjusted to obtain a training picture, the at least one computer readable instruction is executed by a processor To achieve the following steps:

Obtain the preset zoom multiple interval;

Performing random scaling on the first standard image and the second standard image in equal proportions according to the preset scaling factor interval to obtain a randomly scaled picture;

Mapping the randomly zoomed picture on a canvas of a preset size to obtain a target picture with a consistent size;

Randomly adjust brightness, contrast, and saturation of the target picture, respectively, to obtain pictures with random brightness, random contrast, and random saturation;

Random noise is added to the pictures with random brightness, random contrast, and random saturation to obtain training pictures.
The storage medium according to claim 14, wherein, when the initial recognition model is trained according to the backpropagation algorithm and the training picture to obtain a trained recognition model, the at least one computer readable instruction is The processor executes to achieve the following steps:

Input the training picture into the convolutional layer of the initial recognition model to obtain image pixel features;

Input the image pixel features into the recurring layer of the initial recognition model to obtain image time series features;

Inputting the image timing features into the transcription layer of the initial recognition model to obtain a tag sequence;

Using a loss function to calculate the loss value corresponding to the tag sequence;

According to the back propagation algorithm and the loss value, the network parameters of the initial recognition model are updated to obtain a trained recognition model.
The storage medium according to claim 16, wherein, when the network parameters of the initial recognition model are updated according to the backpropagation algorithm and the loss value to obtain a trained recognition model, the at least one computer can The read instruction is executed by the processor to implement the following steps:

According to the back propagation algorithm and the loss value, adjusting the network parameters of the initial recognition model to minimize the loss value to obtain the model to be tested;

Get the preset test set;

Use the test set to test the model to be tested, and determine the accuracy rate at which the model to be tested passes the test;

If the accuracy rate is greater than the preset accuracy rate threshold, it is determined that the model to be tested is a trained recognition model.
The storage medium according to claim 17, wherein the at least one computer readable instruction is further used to implement the following steps when executed by the processor:

If the accuracy rate is less than or equal to the preset accuracy rate threshold, determining that the model to be tested is an untrained recognition model;

Retrain the untrained recognition model.
The storage medium according to claim 14, wherein, after the initial recognition model is trained according to the backpropagation algorithm and the training picture to obtain the trained recognition model, the at least one computer readable instruction is The processor execution is also used to implement the following steps:

Performing tilt correction on the picture to be recognized according to the Hough transform algorithm to obtain a corrected picture;

The inputting the picture to be recognized into the trained recognition model to obtain a recognition result includes:

The correction picture is input into the trained recognition model to obtain a recognition result.
An English handwritten text recognition device, wherein the English handwritten text recognition device includes:

An acquiring module for acquiring a picture collection of English handwritten text lines, wherein the pictures of the picture set of English handwritten text lines include English letters, spaces, and punctuation marks;

The zoom module is used to scale all the pictures in the English handwritten text line picture set in equal proportions according to a preset width threshold to obtain multiple zoom pictures;

The determining module is configured to determine a first standard picture and a picture with a length to be supplemented from the plurality of zoom pictures, wherein the length of the first standard picture is equal to a preset length threshold, and the length of the picture with a length to be supplemented is less than The preset length threshold;

An adding module, configured to add a blank area to the to-be-compensated length picture according to the preset length threshold to obtain a second standard picture, wherein the length of the second standard picture is equal to the preset length threshold;

The adjustment module is used to randomly adjust the first standard picture and the second standard picture to obtain a training picture, wherein the randomly adjusted objects include picture brightness, picture contrast, picture saturation, noise, and picture fonts size;

The training module is used to train the initial recognition model according to the backpropagation algorithm and the training picture to obtain a trained recognition model;

The obtaining module is also used to obtain the picture to be recognized;

The input module is used to input the picture to be recognized into the trained recognition model to obtain a recognition result, where the recognition result includes English letters, spaces and punctuation marks in the picture to be recognized.