WO2022121039A1 - Procédé et appareil de détection basés sur la correction d'inclinaison de carte bancaire, support d'enregistrement lisible, et terminal - Google Patents

Procédé et appareil de détection basés sur la correction d'inclinaison de carte bancaire, support d'enregistrement lisible, et terminal Download PDF

Info

Publication number
WO2022121039A1
WO2022121039A1 PCT/CN2020/141443 CN2020141443W WO2022121039A1 WO 2022121039 A1 WO2022121039 A1 WO 2022121039A1 CN 2020141443 W CN2020141443 W CN 2020141443W WO 2022121039 A1 WO2022121039 A1 WO 2022121039A1
Authority
WO
WIPO (PCT)
Prior art keywords
area
document
image
mask
vertices
Prior art date
Application number
PCT/CN2020/141443
Other languages
English (en)
Chinese (zh)
Inventor
王晓亮
陈建良
田丰
王丹丹
吴昌宇
Original Assignee
广州广电运通金融电子股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 广州广电运通金融电子股份有限公司 filed Critical 广州广电运通金融电子股份有限公司
Publication of WO2022121039A1 publication Critical patent/WO2022121039A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G06V10/243—Aligning, centring, orientation detection or correction of the image by compensating for image skew or non-uniform image deformations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/16—Image preprocessing
    • G06V30/162—Quantising the image signal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/18—Extraction of features or characteristics of the image
    • G06V30/1801—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes or intersections
    • G06V30/18067—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes or intersections by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40—Document-oriented image-based pattern recognition
    • G06V30/41—Analysis of document content
    • G06V30/414—Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Definitions

  • the invention relates to the technical field of information detection or intelligent vision, in particular to a bank card tilt correction detection method, device, readable storage medium and terminal.
  • image recognition technology is gradually applied in security, military, medical, intelligent transportation and other fields, and technologies such as face recognition and fingerprint recognition are increasingly used in public security, finance, aerospace and other security fields.
  • image recognition is mainly used in the reconnaissance and identification of targets, through automatic image recognition technology to identify and strike enemy targets; in the medical field, various medical image analysis and diagnosis can be carried out through image recognition technology, On the one hand, it can greatly reduce the cost of medical treatment, and on the other hand, it can also help to improve the quality and efficiency of medical care; in the field of transportation, it can not only perform license plate recognition, but also be applied to the cutting-edge field of autonomous driving to achieve a clear view of roads, vehicles and pedestrians.
  • Deep learning method This method uses a large amount of labeled data to train the deep network in the model training stage, fits the network parameters, and realizes the modeling of the OCR (Optical Character Recognition, Optical Character Recognition) detection algorithm.
  • OCR Optical Character Recognition, Optical Character Recognition
  • the image is used as the input of the network, and the character region detection is realized through the network forward reasoning.
  • This method is currently a popular character detection method, but for the identification number detection task, this method has the following defects: (1) The non-document area image also participates in the network reasoning process, which wastes computing resources on the one hand; False detection of characters in the region existence requires additional processing logic to be eliminated; (2) This scheme consumes more computing resources, and the training and reasoning time is longer than this proposal; (3) Due to the inexplicability of the neural network, this method The frame of the positioned character area cannot accurately locate the smallest bounding rectangle of the character, and even cuts off part of the character area. That is, the traditional optical recognition (OCR) technology of document images is mainly used for high-definition scanned images. This method requires the recognized images to have clean Background, use standard print and have high resolution. However, in natural scenes, there are problems such as large text background noise, irregular text distribution, and the influence of natural light sources. The detection rate of OCR technology in actual natural scenes is not ideal, and identification of documents such as documents brings pressure to the character recognition in the subsequent steps.
  • OCR optical recognition
  • the purpose of the present invention is to provide a bank card tilt correction detection method, device, readable storage medium and terminal, which can solve the above problems.
  • BTC Bankcard Tilt Correction
  • a method for detecting bank card tilt correction under complex background comprises the following steps:
  • the first step, model training label the original data and generate labels, count the document size according to the generated label files, and use the original data and label files to train the segmentation model;
  • the second step uses the deep learning model to find the corresponding potential document area for the picture input through the image acquisition unit, and obtains a preliminary and rough document area mask;
  • the third step standardization, fine-tune the rough mask obtained in the first step to obtain a high-quality document area mask, use the mask to extract the document area in the original image, and perform affine correction transformation on the obtained document photo , convert it to the preset ID photo size, and output the corrected ID photo.
  • the first step of model training includes the following steps:
  • S11 Determine the certificate area, and find the certificate area in the picture of the original data through manual annotation
  • S14 trains the segmentation model, and uses the original data and the generated annotation files to train the segmentation model.
  • step S14 the input picture and the corresponding annotation file have the same size; and the json file is converted into a corresponding 0-1 binary mask before training, wherein the area with a pixel of 1 represents the certificate area, The area with pixel 0 represents the background area.
  • the initial certificate inspection in the second step includes the following steps:
  • S21 extracts features, after inputting a picture, scales the picture to a size suitable for the input picture of the segmentation network, and then uses the Unet network model to extract depth features from the input data to obtain a feature map;
  • S22 calculates the probability, carries out two-class judgment on the feature of each position in the feature map, obtains the probability value that the feature of each position belongs to the document area, and obtains the probability distribution map belonging to the document area;
  • S24 rough segmentation mask upsample the 0-1 mask image to the same size as the original input image, and obtain a preliminary document rough segmentation mask image
  • S25 Legal area screening count the area a of each isolated document area in the rough segmentation mask, if a ⁇ -3 ⁇ , the area a is considered to be an illegal area, and it is removed from the rough segmentation mask to pass the legal area Filtering will filter some error areas.
  • fine-grained mask correction is performed on the legal regions in the mask image filtered in the first step, including the following steps:
  • the contour feature is a binary mask image, the whole is a closed irregular curve, and the binary mask image does not change the properties of the rectangular convex set of the document photo;
  • S32 obtains the convex hull of the contour, obtains the minimum convex hull of the contour on the basis of the original contour, fills in the missing area of the partial segmentation, and at the same time smoothes the edge of the contour;
  • the sorting of the four vertices is determined through the following steps: 1) Obtain the coordinates of the center point according to the coordinates of the four vertices; 2) Establish a polar coordinate system with the center point, and construct a point from the center point to each vertex 3) Sort the four vertices according to the size of the included angle from large to small; 4) Find the upper left corner of the document area, and use the minimum coordinate value
  • the vertex of the sum is the upper left vertex, and the coordinate order is rearranged with the upper left vertex as the starting point, and arranged in the order of "upper left - upper right - lower right - lower left";
  • step S33 the minimum detected straight line length for performing straight line fitting on the convex hull by Hough transform is set to 100, and the maximum interval between straight lines is set to 20.
  • step S36 the specific algorithm of K-means is:
  • the invention also provides a certificate detection device, the device includes an acquisition input unit, an image processing unit, an information extraction unit, and an information output unit connected by telecommunication; wherein, the acquisition input unit acquires the detection picture of the certificate to be detected and the information output unit through the camera assembly.
  • the standard registration picture; the image processing unit processes the input picture through the deep learning algorithm and the image processing algorithm in the processor, and sequentially obtains the preliminary rough document area mask, the document area refined mask, and the deducted original document.
  • the image area and the corrected image after affine transformation correction; the information extraction unit, the category and information of the corrected image are corrected by the information extraction algorithm in the processor; the information output unit, the processor extracts the category and information results of the input picture on the display Display and store to memory.
  • the present invention also provides a computer-readable storage medium on which computer instructions are stored, and when the computer instructions are executed, the steps of the aforementioned method are performed.
  • the present invention also provides a terminal, including a memory and a processor, the memory stores a registered picture and a computer instruction that can be run on the processor, and the processor executes the method of the foregoing method when the processor runs the computer instruction. step.
  • the beneficial effect of the present invention is that: by combining the deep learning technology and the traditional image processing method with the bank card tilt correction technology (Bankcard Tilt Correction, BTC) of the present application, the advantages of the two are fully integrated, and the advantages of the two are fully integrated.
  • BTC Bankcard Tilt Correction
  • Fig. 1 is the flow chart of the bank card tilt correction detection method under the complex background of the present invention
  • Figure 2 is a schematic diagram of model training
  • FIG. 3 is a simplified flow chart of the BTC testing phase
  • Fig. 4 is the method flow chart of the initial inspection of the certificate
  • Figure 5 is a flow chart of document image standardization.
  • FIG. 1-Fig. 5 A method for detecting bank card tilt correction under complex background is shown in Fig. 1-Fig. 5. The method includes the following steps.
  • the first step, model training label the original data and generate labels, count the document size according to the generated label files, and use the original data and label files to train the segmentation model.
  • the second step is the initial inspection of the document.
  • the deep learning model is used to find the corresponding potential document area, and a preliminary and rough document area mask is obtained.
  • the third step standardization, fine-tune the rough mask obtained in the first step to obtain a high-quality document area mask, use the mask to extract the document area in the original image, and perform affine correction transformation on the obtained document photo , convert it to the preset ID photo size, and output the corrected ID photo.
  • BTC relies on the powerful feature extraction ability of deep learning, so it needs to train related models before it is officially used.
  • a batch of raw data to be trained first find the area of documents such as bank cards in the picture by manual annotation. Specifically, for each document in the picture, the four vertices of the document are marked, and the coordinate positions of the vertices are saved as a json file. Next, according to the generated annotation file, the area size s of each document area is counted, which is designed to serve the subsequent testing phase. It is verified by an example that the size of the document photo area in the original data conforms to the Gaussian distribution, namely: s ⁇ N( ⁇ , ⁇ 2 ).
  • the mean ⁇ and standard deviation ⁇ of the Gaussian distribution are calculated.
  • the segmentation model is trained using the raw data and the generated annotation files. It is worth noting that in the specific training, it is necessary to keep the input image and the corresponding annotation file with the same size. Therefore, it is also necessary to convert the marked json file into a corresponding 0-1 binary mask map, in which the area with a pixel of 1 represents the document area, and the area with a pixel of 0 represents the background area.
  • model training steps of the first step are as follows.
  • S11 determines the certificate area, and finds the certificate area in the picture of the original data through manual annotation.
  • S12 Vertex labeling generates labels, labels the four vertices of the document in the document area, and saves the coordinate positions of the vertices in the form of json files to generate labels.
  • JSON JavaScript Object Notation
  • JSON is a lightweight data interchange format. Easy for humans to read and write. It is also easy to parse and generate by machine. It is based on JavaScript Programming Language, a subset of Standard ECMA-262 3rd Edition-December 1999. JSON is a sequence of tokens, consisting of six constructed characters, strings, numbers, and three literal names. Because of this, the coordinate annotation applied to this scheme can be well matched.
  • S14 trains the segmentation model, and uses the original data and the generated annotation files to train the segmentation model.
  • BTC is a two-stage, coarse-to-fine segmentation optimization model (two-stage and coarse-to-fine refinement segmentation).
  • the deep learning model is used to find the corresponding potential document area for the input picture, and a preliminary and relatively rough document area mask is obtained;
  • the second stage using traditional image processing technology,
  • the rough mask of the first stage is refined and corrected to obtain a high-quality document area mask, and the document photo is extracted from the original image by using the mask.
  • Set the ID photo size is a two-stage, coarse-to-fine segmentation optimization model (two-stage and coarse-to-fine refinement segmentation).
  • the first stage is the initial inspection of documents.
  • the goal of finding the document area is mainly completed by several sub-operations of extracting features, calculating probability, and threshold truncation, and finally obtains a preliminary rough segmentation mask.
  • the user inputs the picture, it is scaled to the input picture size suitable for the segmentation network, and then the classical Unet network model is used to extract the depth features of the input data;
  • the two-class judgment is to obtain the probability value that the feature of each position belongs to the certificate area. So far, a probability distribution map belonging to the certificate area is obtained; then, the probability distribution map is binarized according to the preset threshold.
  • S21 extracts features, after inputting a picture, scales the picture to a size suitable for the input picture of the segmentation network, and then uses the Unet network model to extract depth features from the input data to obtain a feature map.
  • S22 calculates the probability, performs binary classification judgment on the feature of each position in the feature map, obtains the probability value that the feature of each position belongs to the certificate area, and obtains a probability distribution map belonging to the certificate area.
  • the 0-1 mask image is upsampled to the same size as the original input image, and a preliminary document rough segmentation mask image is obtained.
  • S25 Legal area screening count the area a of each isolated document area in the rough segmentation mask, if a ⁇ -3 ⁇ , the area a is considered to be an illegal area, and it is removed from the rough segmentation mask to pass the legal area Filtering will filter some error areas.
  • the Unet network model belongs to the segmentation network.
  • Unet draws on the FCN network, and its network structure includes two symmetrical parts: the first part of the network is the same as the ordinary convolutional network, using 3x3 convolution and pooling downsampling, which can capture The context information in the image (that is, the relationship between pixels); the latter part of the network is basically symmetrical with the former, using 3x3 convolution and upsampling to achieve the purpose of output image segmentation.
  • feature fusion is also used in the network, and the features of the previous part of the downsampling network are fused with the features of the latter part of the upsampling part to obtain more accurate context information and achieve a better segmentation effect.
  • Unet uses a weighted softmax loss function, which has its own weight for each pixel, which makes the network pay more attention to the learning of edge pixels. Using this model is more suitable for the slight uneven change of the edge of the document which is not straight.
  • the second stage is standardization. On the basis of the first stage, the refinement mask refinement of the second stage is performed. As shown in Figure 5, all legal regions in the mask map obtained in the first stage must be corrected one by one. In the second step of standardization, for each legal document area, that is, the refined mask correction is performed on the legal area in the mask image after the screening in the first step, see FIG. 5 , including the following steps.
  • the contour feature is a binary mask image
  • the whole is a closed irregular curve
  • the binary mask image does not change the properties of the rectangular convex set of the ID photo.
  • Convex sets are still convex sets after affine transformation.
  • One of the good properties of ID photo is that it is a regular rectangular shape, which is a standard convex set. No matter what affine transformation the convex set undergoes in the collection stage, the properties of the convex set cannot be changed.
  • S32 obtains the convex hull of the contour, obtains the minimum convex hull of the contour on the basis of the original contour, fills in the missing area of the partial segmentation, and smoothes the edge of the contour at the same time.
  • the minimum convex hull of the contour is obtained on the basis of the original contour, and the missing area of the partial segmentation is filled, and the contour edge is smoother at the same time.
  • step S33 line fitting, using Hough transform to perform straight line fitting on an irregular convex polygon composed of multiple line segments of the convex hull to describe the convex hull.
  • the minimum detected straight line length for performing straight line fitting on the convex hull by Hough transform is set to 100, and the maximum interval between straight lines is set to 20.
  • Hough transform is a feature extraction, which is widely used in image analysis, computer vision and digital image processing. Extract features in objects, such as lines. This scheme uses it to accurately parse the defined document edge line.
  • S34 finds the vertices, reads all the legal straight lines in the straight line fitting to find the intersection points in pairs, so as to find the distribution range of the four vertices of the certificate photo.
  • all the legal straight lines detected in S33 can be straight lines. analytic expression. For all legal straight lines, read them to find the intersection points. This step is to find the distribution range of the four vertices of the ID photo. And in the process of finding the vertices, the case where the two lines are parallel is not considered.
  • a filter condition is set to check the legitimacy of the vertex.
  • the tolerance value tol is set in the filter condition, the abscissa [0-tol, width+tol], and the ordinate [0-tol, height+tol] are defined as legal vertex coordinates , where width and height represent the width and height of the original image.
  • the tolerance value tol is set to 50.
  • min(x crosspoint , width) will make the maximum value of x crosspoint not exceed the width of the original image, and the minimum value of max(min(x crosspoint , width), 0) cannot be less than 0;
  • min(y crosspoint , height) will make the maximum value of y crosspoint not exceed the height of the original image, and the minimum value of max(min(y corsspoint , height), 0) cannot be less than 0.
  • S36 vertex clustering compared with the standard bank card, there are four vertices. According to all the legal vertices that have been obtained, all vertices are clustered into four categories through the unsupervised clustering algorithm K-means, and the centroid of each category is a certain The coordinates of the vertex, a total of four vertex coordinates are obtained.
  • K-means is the most commonly used clustering algorithm based on Euclidean distance, which is numerical, unsupervised, non-deterministic, and iterative, and the algorithm aims to minimize an objective function - the squared error function (all The sum of the distance between the observation point and its center point), it believes that the closer the distance between the two targets, the greater the similarity. Due to its excellent speed and good scalability, the Kmeans clustering algorithm can be regarded as the most famous clustering algorithm method.
  • step 4) of step S37 the sum of the coordinate values of the upper left coordinate point is the smallest, and the vertex of the sum of the smallest coordinate value is the upper left vertex, and the coordinate order is rearranged from this as the starting point to determine the four vertexes. order.
  • the invention also provides a certificate detection device, which includes an acquisition input unit, an image processing unit, an information extraction unit, and an information output unit connected by telecommunication.
  • the input unit and obtain the inspection picture and standard registration picture of the certificate to be inspected through the camera component; the obtaining unit uses hardware equipment, including but not limited to mobile phones, IPAD, ordinary cameras, CCD industrial cameras, scanners, etc., to image the front of the certificate
  • hardware equipment including but not limited to mobile phones, IPAD, ordinary cameras, CCD industrial cameras, scanners, etc., to image the front of the certificate
  • the collected image should completely include the four borders of the document, and the inclination should not exceed plus or minus 20°, and the human eye can distinguish the document number and the edge straight line.
  • the image processing unit processes the input image through the deep learning algorithm and the image processing algorithm in the processor, and sequentially obtains the preliminary rough document area mask, the document area refined mask, the deducted original image area and the affine Transform the rectified image after rectification.
  • the collected image is an image collected by a camera, which can be a static image (that is, an image collected separately), or an image in a video (that is, an image selected from the collected video according to preset standards or randomly
  • An image of the present invention can be used as the image source of the document of the present invention, and the embodiment of the present invention has no restrictions on all attributes such as the source, nature, size, etc. of the image.
  • the information extraction unit will correct the category and information of the image through the information extraction algorithm in the processor.
  • the processor displays the category and information result extracted from the input picture on the display and stores it in the memory.
  • the display includes but is not limited to the display screen of a tablet computer, computer, mobile phone, etc., which compares and classifies the certificates extracted by the processor.
  • embodiments of the present disclosure may also utilize, for example, but not limited to, image processing-based document detection algorithms (eg, edge detection, mathematical morphology, texture analysis-based localization, line detection, and edge detection). Statistical method, genetic algorithm, Hough transform and contour method, method based on wavelet transform, etc.), to perform document detection on the collected image.
  • image processing-based document detection algorithms eg, edge detection, mathematical morphology, texture analysis-based localization, line detection, and edge detection.
  • Statistical method genetic algorithm, Hough transform and contour method, method based on wavelet transform, etc.
  • the neural network when edge detection is performed on the collected image through the neural network, the neural network can be trained by using the sample image in advance, so that the trained neural network can effectively detect the edge straight lines in the image.
  • the present invention also provides a computer-readable storage medium on which computer instructions are stored, and when the computer instructions are executed, the steps of the aforementioned method are performed.
  • a computer-readable storage medium on which computer instructions are stored, and when the computer instructions are executed, the steps of the aforementioned method are performed.
  • Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
  • computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.
  • the present invention also provides a terminal, including a memory and a processor, the memory stores a registered picture and a computer instruction that can be run on the processor, and the processor executes the method of the foregoing method when the processor runs the computer instruction. step.
  • a terminal including a memory and a processor
  • the memory stores a registered picture and a computer instruction that can be run on the processor
  • the processor executes the method of the foregoing method when the processor runs the computer instruction. step.
  • the embodiments of the present application may be provided as methods, apparatuses, systems or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including, but not limited to, disk storage, CD-ROM, optical storage, etc.

Abstract

Procédé et appareil de détection basés sur la correction d'inclinaison de carte bancaire (BTC), support d'enregistrement lisible, et terminal. À l'aide de la technologie BTC en association avec la technologie d'apprentissage profond et d'un procédé de traitement d'images classique, les avantages des deux solutions sont entièrement intégrés, et, pour une large série d'images d'entrée d'utilisateur présentant des scènes complexes, des résultats de segmentation et de correction de certificat à précision élevée et à robustesse élevée peuvent être obtenus, ce qui établit une base pour la détection de certificat, la classification et l'extraction d'informations ultérieures, et ce qui améliore la portée d'application de la reconnaissance de certificat ; la présente invention peut être largement appliquée dans les domaines de la sécurité, des finances et analogues.
PCT/CN2020/141443 2020-12-10 2020-12-30 Procédé et appareil de détection basés sur la correction d'inclinaison de carte bancaire, support d'enregistrement lisible, et terminal WO2022121039A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011458177.8 2020-12-10
CN202011458177.8A CN112686812B (zh) 2020-12-10 2020-12-10 银行卡倾斜矫正检测方法、装置、可读存储介质和终端

Publications (1)

Publication Number Publication Date
WO2022121039A1 true WO2022121039A1 (fr) 2022-06-16

Family

ID=75449185

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/141443 WO2022121039A1 (fr) 2020-12-10 2020-12-30 Procédé et appareil de détection basés sur la correction d'inclinaison de carte bancaire, support d'enregistrement lisible, et terminal

Country Status (2)

Country Link
CN (1) CN112686812B (fr)
WO (1) WO2022121039A1 (fr)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114882489A (zh) * 2022-07-07 2022-08-09 浙江智慧视频安防创新中心有限公司 一种对旋转车牌进行水平校正方法、装置、设备及介质
CN115272206A (zh) * 2022-07-18 2022-11-01 深圳市医未医疗科技有限公司 医学图像处理方法、装置、计算机设备及存储介质
CN115457559A (zh) * 2022-08-19 2022-12-09 上海通办信息服务有限公司 一种将文本及证照类图片智能摆正的方法、装置和设备
CN117095423A (zh) * 2023-10-20 2023-11-21 上海银行股份有限公司 一种银行单据字符的识别方法及装置
CN117315664A (zh) * 2023-09-18 2023-12-29 山东博昂信息科技有限公司 一种基于图像序列的废钢斗号码识别方法
CN117409261A (zh) * 2023-12-14 2024-01-16 成都数之联科技股份有限公司 一种基于分类模型的元件角度分类方法及系统

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113033543B (zh) * 2021-04-27 2024-04-05 中国平安人寿保险股份有限公司 曲形文本识别方法、装置、设备及介质
CN113344000A (zh) * 2021-06-29 2021-09-03 南京星云数字技术有限公司 证件翻拍识别方法、装置、计算机设备和存储介质
CN113870262B (zh) * 2021-12-02 2022-04-19 武汉飞恩微电子有限公司 基于图像处理的印刷电路板分类方法、装置及储存介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108537219A (zh) * 2018-03-20 2018-09-14 上海眼控科技股份有限公司 一种用于财务报表外框的智能检测方法及装置
CN108682015A (zh) * 2018-05-28 2018-10-19 科大讯飞股份有限公司 一种生物图像中的病灶分割方法、装置、设备及存储介质
JP2018199473A (ja) * 2017-05-30 2018-12-20 株式会社Ｓｏｋｅｎ 操舵角決定装置、自動運転車
CN110458161A (zh) * 2019-07-15 2019-11-15 天津大学 一种结合深度学习的移动机器人门牌定位方法
CN111027564A (zh) * 2019-12-20 2020-04-17 长沙千视通智能科技有限公司 基于深度学习一体化的低照度成像车牌识别方法及装置

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110110715A (zh) * 2019-04-30 2019-08-09 北京金山云网络技术有限公司 文本检测模型训练方法、文本区域、内容确定方法和装置
CN110866871A (zh) * 2019-11-15 2020-03-06 深圳市华云中盛科技股份有限公司 文本图像矫正方法、装置、计算机设备及存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018199473A (ja) * 2017-05-30 2018-12-20 株式会社Ｓｏｋｅｎ 操舵角決定装置、自動運転車
CN108537219A (zh) * 2018-03-20 2018-09-14 上海眼控科技股份有限公司 一种用于财务报表外框的智能检测方法及装置
CN108682015A (zh) * 2018-05-28 2018-10-19 科大讯飞股份有限公司 一种生物图像中的病灶分割方法、装置、设备及存储介质
CN110458161A (zh) * 2019-07-15 2019-11-15 天津大学 一种结合深度学习的移动机器人门牌定位方法
CN111027564A (zh) * 2019-12-20 2020-04-17 长沙千视通智能科技有限公司 基于深度学习一体化的低照度成像车牌识别方法及装置

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114882489A (zh) * 2022-07-07 2022-08-09 浙江智慧视频安防创新中心有限公司 一种对旋转车牌进行水平校正方法、装置、设备及介质
CN115272206A (zh) * 2022-07-18 2022-11-01 深圳市医未医疗科技有限公司 医学图像处理方法、装置、计算机设备及存储介质
CN115457559A (zh) * 2022-08-19 2022-12-09 上海通办信息服务有限公司 一种将文本及证照类图片智能摆正的方法、装置和设备
CN115457559B (zh) * 2022-08-19 2024-01-16 上海通办信息服务有限公司 一种将文本及证照类图片智能摆正的方法、装置和设备
CN117315664A (zh) * 2023-09-18 2023-12-29 山东博昂信息科技有限公司 一种基于图像序列的废钢斗号码识别方法
CN117315664B (zh) * 2023-09-18 2024-04-02 山东博昂信息科技有限公司 一种基于图像序列的废钢斗号码识别方法
CN117095423A (zh) * 2023-10-20 2023-11-21 上海银行股份有限公司 一种银行单据字符的识别方法及装置
CN117095423B (zh) * 2023-10-20 2024-01-05 上海银行股份有限公司 一种银行单据字符的识别方法及装置
CN117409261A (zh) * 2023-12-14 2024-01-16 成都数之联科技股份有限公司 一种基于分类模型的元件角度分类方法及系统
CN117409261B (zh) * 2023-12-14 2024-02-20 成都数之联科技股份有限公司 一种基于分类模型的元件角度分类方法及系统

Also Published As

Publication number Publication date
CN112686812B (zh) 2023-08-29
CN112686812A (zh) 2021-04-20

Similar Documents

Publication Publication Date Title
WO2022121039A1 (fr) Procédé et appareil de détection basés sur la correction d'inclinaison de carte bancaire, support d'enregistrement lisible, et terminal
US20200364443A1 (en) Method for acquiring motion track and device thereof, storage medium, and terminal
Gou et al. Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines
Zang et al. Vehicle license plate recognition using visual attention model and deep learning
Silva et al. A flexible approach for automatic license plate recognition in unconstrained scenarios
CN101142584B (zh) 面部特征检测的方法
CN108334881B (zh) 一种基于深度学习的车牌识别方法
CN111310662B (zh) 一种基于集成深度网络的火焰检测识别方法及系统
CN104751142A (zh) 一种基于笔划特征的自然场景文本检测算法
Zhang et al. Road recognition from remote sensing imagery using incremental learning
CN110298376A (zh) 一种基于改进b-cnn的银行票据图像分类方法
Dehshibi et al. Persian vehicle license plate recognition using multiclass Adaboost
WO2022121025A1 (fr) Procédé et appareil d'augmentation et de réduction de catégorie de certificat, support d'enregistrement lisible et terminal
CN112101208A (zh) 高龄老人特征串联融合手势识别方法及装置
CN105335760A (zh) 一种图像数字字符识别方法
Gawande et al. SIRA: Scale illumination rotation affine invariant mask R-CNN for pedestrian detection
CN110363196B (zh) 一种倾斜文本的文字精准识别的方法
WO2022121021A1 (fr) Procédé et appareil de détection de numéro de carte d'identité, et support de stockage lisible et terminal
Mei et al. A novel framework for container code-character recognition based on deep learning and template matching
CN111695373A (zh) 斑马线的定位方法、系统、介质及设备
CN111062393B (zh) 一种基于谱聚类的自然场景汉字分割方法
CN110910497B (zh) 实现增强现实地图的方法和系统
CN109325487B (zh) 一种基于目标检测的全种类车牌识别方法
CN107330436B (zh) 一种基于尺度判据的全景图像sift优化方法
Ning Vehicle license plate detection and recognition

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20964968

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20964968

Country of ref document: EP

Kind code of ref document: A1