WO2020052085A1 - Procédé et dispositif de détection de texte vidéo et support d'informations lisible par ordinateur - Google Patents

Procédé et dispositif de détection de texte vidéo et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2020052085A1
WO2020052085A1 PCT/CN2018/117715 CN2018117715W WO2020052085A1 WO 2020052085 A1 WO2020052085 A1 WO 2020052085A1 CN 2018117715 W CN2018117715 W CN 2018117715W WO 2020052085 A1 WO2020052085 A1 WO 2020052085A1
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WO
WIPO (PCT)
Prior art keywords
image block
image
score
text
text information
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PCT/CN2018/117715
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English (en)
Chinese (zh)
Inventor
周多友
王长虎
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北京字节跳动网络技术有限公司
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Publication of WO2020052085A1 publication Critical patent/WO2020052085A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • 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

Definitions

  • the present disclosure relates to the technical field of information processing, and in particular, to a video text detection method, device, and computer-readable storage medium.
  • the technical problem solved by the present disclosure is to provide a video text detection method to at least partially solve the technical problem that the OCR has a poor recognition effect and low recognition accuracy when recognizing small characters.
  • a video text detection device, a video text detection hardware device, a computer-readable storage medium, and a video text detection terminal are also provided.
  • a video text detection method includes:
  • the step of determining whether text information is included in the video to be detected according to a text detection result on the image block includes:
  • the method further includes:
  • a deep learning classification algorithm is used to perform training and learning on the labeled training samples to obtain an image classifier.
  • the step of segmenting the to-be-detected picture extracted from the to-be-detected video to obtain at least one image block includes:
  • the method further includes:
  • Text detection is performed on the image block by the image classifier, and a text detection result of the image block is determined according to a classification result of the image classifier.
  • the steps of performing text detection on the image block by the image classifier, and determining the text detection result of the image block based on the classification result of the image classifier include:
  • a text detection result of the image block is determined according to the score.
  • the step of determining a text detection result of the image block according to the score includes:
  • the score exceeds a preset score, determine that the image block contains text information; or, select a maximum score from the score, and if the maximum score exceeds the preset score, determine the maximum score.
  • the image block contains text information; or, if the score is smaller than a preset score, it is determined that the image block contains text information; or, a minimum score is selected from the scores, and if the minimum score is If the value is less than the preset score, it is determined that the image block contains text information.
  • the step of performing text detection on the image block by the image classifier, and determining a text detection result of the image block according to a classification result of the image classifier includes:
  • the output result is used as a text detection result of the image block.
  • a video text detection device includes:
  • a picture block module configured to block the pictures to be detected extracted from the videos to be detected to obtain at least one image block
  • a text determining module is configured to determine whether text information is included in the video to be detected according to a text detection result of the image block.
  • the text determination module is specifically configured to: perform text detection on each image block; if it is detected that any image block contains text information, determine that the video to be detected includes text information.
  • the device further includes:
  • a classifier training module configured to block pictures that have known text information and / or pictures that do not contain text information to obtain at least one image block as a training sample; and perform training on the training sample according to whether text information is included Labeling; using deep learning classification algorithms to train and learn the labeled training samples to obtain an image classifier.
  • the picture segmentation module is specifically configured to: input the picture to be detected into the image classifier, and divide the picture to be detected by the image classifier to obtain at least one image block;
  • the device further includes:
  • a text detection module is configured to perform text detection on the image block through the image classifier, and determine a text detection result of the image block according to a classification result of the image classifier.
  • the character detection module includes:
  • a scoring unit configured to score each image block through the image classifier to obtain a score value of each image block
  • a character detection unit configured to determine a character detection result of the image block according to the score.
  • the character detection unit is specifically configured to:
  • the score exceeds a preset score, determine that the image block contains text information; or, select a maximum score from the score, and if the maximum score exceeds a preset score, determine the image block. Contains text information; or, if the score is less than a preset score, determine that the image block contains text information; or select a minimum score from the scores, and if the minimum score is less than a preset score , It is determined that the image block contains text information.
  • the text detection module is specifically configured to perform text detection on each image block through the image classifier, and directly output any one of the following results through the image classifier: including text information and not including text information; The output result is used as a text detection result of the image block.
  • a video text detection hardware device includes:
  • Memory for storing non-transitory computer-readable instructions
  • a processor configured to run the computer-readable instructions, so that the processor, when executed, implements the steps described in any of the foregoing technical solutions of a video text detection method.
  • a computer-readable storage medium is used for storing non-transitory computer-readable instructions.
  • the computer is caused to execute any of the technical solutions of the video text detection method described above. The steps described.
  • a video text detection terminal includes any of the video text detection devices described above.
  • Embodiments of the present disclosure provide a video text detection method, a video text detection device, a video text detection hardware device, a computer-readable storage medium, and a video text detection terminal.
  • the video text detection method includes segmenting a to-be-detected picture extracted from the to-be-detected video to obtain at least one image block; and determining whether the to-be-detected video contains text information based on a text detection result of the image block.
  • the embodiment of the present disclosure first divides the to-be-detected picture extracted from the to-be-detected video into at least one image block, and then determines whether the to-be-detected video contains text information according to a text detection result on the image block. Improve text detection accuracy.
  • FIG. 1a is a schematic flowchart of a video text detection method according to an embodiment of the present disclosure
  • FIG. 1b is a schematic flowchart of a video text detection method according to another embodiment of the present disclosure.
  • 1c is a schematic flowchart of a video text detection method according to another embodiment of the present disclosure.
  • FIG. 2a is a schematic structural diagram of a video text detection device according to an embodiment of the present disclosure
  • FIG. 2b is a schematic structural diagram of a video text detection device according to another embodiment of the present disclosure.
  • FIG. 3 is a schematic structural diagram of a video text detection hardware device according to an embodiment of the present disclosure.
  • FIG. 4 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present disclosure.
  • FIG. 5 is a schematic structural diagram of a video text detection terminal according to an embodiment of the present disclosure.
  • the video text detection method mainly includes the following steps S1 to S2. among them:
  • Step S1 Divide the detected pictures extracted from the videos to be detected to obtain at least one image block.
  • the picture to be detected may be one frame or multiple frames. When the picture to be detected is multiple frames, the pictures to be detected are divided into blocks.
  • the number of image blocks or the size of the image blocks may be specifically determined according to the size of the picture to be detected. Specifically, in order to improve the accuracy of text detection, a plurality of pictures of different sizes can be divided into blocks in advance, and text detection can be performed, and the optimal number or size of blocks can be determined according to the accuracy of text detection.
  • Step S2 Determine whether text information is included in the video to be detected according to the text detection result of the image block.
  • the text information includes, but is not limited to, any one or combination of numbers, Chinese characters, and foreign languages.
  • the text information can be enlarged by segmentation, thereby improving the accuracy of text detection.
  • At least one image block is obtained by dividing the to-be-detected picture extracted from the to-be-detected video, and then determining whether the to-be-detected video contains text information based on the text detection result of the image block, which can improve the accuracy of text detection.
  • step S2 includes:
  • the text detection method in the prior art can be used to perform text detection on the image block. Because the image to be detected is divided, the text contained in the image block may be incomplete. For example, the detected image block may be incomplete. Containing only a part of a text or a part of a text, it is determined that the image block contains text information.
  • At least one image block is obtained by dividing the to-be-detected picture extracted from the to-be-detected video, and each image block is subjected to text detection. If any image block is detected to contain text information, the video to be detected is determined. The text information is included in the block, and the text information contained in the picture to be detected can be enlarged by the block, thereby improving the accuracy of the text detection.
  • the method in this embodiment further includes:
  • S3 Block pictures that are known to contain text information and / or pictures that do not contain text information to obtain at least one image block as a training sample.
  • S4 Annotate training samples according to whether text information is included.
  • each image block needs to be labeled. For example, an image block containing text information is marked with 1 and an image block without text information is marked with 0.
  • S5 The deep learning classification algorithm is used to train and learn the labeled training samples to obtain an image classifier.
  • the deep learning classification algorithms include, but are not limited to, any of the following: Naive Bayes algorithm, artificial neural network algorithm, genetic algorithm, K-Nearest Neighbor (KNN) classification algorithm, clustering algorithm, and the like.
  • KNN K-Nearest Neighbor
  • the image classifier obtained through this embodiment not only has an automatic block function, but also can directly determine whether each image block contains text information.
  • step S1 specifically includes:
  • the picture to be detected is input to an image classifier, and the picture to be detected is divided into blocks by the image classifier to obtain at least one image block.
  • S6 Perform text detection on the image block through the image classifier, and determine the text detection result of the image block according to the classification result of the image classifier.
  • step S6 specifically includes:
  • S61 Score each image block by an image classifier to obtain a score value of each image block.
  • the score may be a normalized score, for example, any value from 0 to 100 or 0-1.
  • S62 Determine the text detection result of the image block according to the score.
  • step S62 specifically includes:
  • the score exceeds the preset score, determine that the image block contains text information; or, select the maximum score from the score, and if the maximum score exceeds the preset score, determine that the image block contains text information; or, If the score is smaller than the preset score, it is determined that the image block contains text information; or, the minimum score is selected from the scores; if the minimum score is smaller than the preset score, the image block is determined to contain text information.
  • a scoring rule can be set in advance. For example, the larger the score, the higher the probability that the character information is included, or the smaller the score, the higher the possibility that the character information is included. Based on the scoring rules set above, it is determined whether the image block contains text information.
  • step S6 specifically includes:
  • S63 Perform text detection on each image block through the image classifier, and directly output any one of the following results through the image classifier: including text information and not including text information.
  • the following is a device embodiment of the present disclosure.
  • the device embodiment of the present disclosure can be used to perform the steps implemented by the method embodiments of the present disclosure.
  • Only parts related to the embodiments of the present disclosure are shown. Specific technical details are not disclosed. Reference is made to the method embodiments of the present disclosure.
  • an embodiment of the present disclosure provides a video text detection device.
  • the device can perform the steps in the foregoing embodiment of the video text detection method.
  • the device mainly includes: a picture block module 21 and a text determination module 22; wherein the picture block module 21 is configured to block a picture to be detected extracted from a video to be detected to obtain at least one image Block; the text determining module 22 is configured to determine whether text information is included in a video to be detected according to a text detection result on an image block.
  • the picture to be detected may be one frame or multiple frames. When the picture to be detected is multiple frames, the pictures to be detected are divided into blocks.
  • the number of image blocks or the size of the image blocks may be specifically determined according to the size of the picture to be detected. Specifically, in order to improve the accuracy of text detection, a plurality of pictures of different sizes can be divided into blocks in advance, and text detection can be performed, and the optimal number or size of blocks can be determined according to the accuracy of text detection.
  • the text information includes, but is not limited to, any one or combination of numbers, Chinese characters, and foreign languages.
  • the text information can be enlarged by segmentation, thereby improving the accuracy of text detection.
  • the picture segmentation module 21 is used to divide the picture to be detected extracted from the video to be detected to obtain at least one image block, and then the text determination module 22 determines whether the video to be detected is based on the text detection result of the image block. Contains text information to improve text detection accuracy.
  • the text determination module 22 is specifically configured to: perform text detection on each image block; if it is detected that any image block contains text information, determine that the video to be detected contains text information.
  • the text determination module 22 may use the text detection methods in the prior art to perform text detection on image blocks. Because the pictures to be detected are divided, the text contained in the image blocks may be incomplete, for example, the detected image blocks It may contain only a part of a character or a part of a character. At this time, it is determined that the image block contains character information.
  • the picture segmentation module 21 is used to segment the pictures to be detected extracted from the video to be detected to obtain at least one image block
  • the text determination module 22 is used to perform text detection on each image block. If any image block is detected, If text information is included in the video, it is determined that the text information is included in the video to be detected. Since the text information contained in the image to be detected can be enlarged by segmentation, the accuracy of text detection is improved.
  • the apparatus in this embodiment further includes: a classifier training module 23; wherein the classifier training module 23 is configured to perform a process on pictures and / or pictures that already contain text information.
  • the pictures that do not contain text information are divided into blocks to obtain at least one image block as training samples; the training samples are labeled according to whether they contain text information; the deep learning classification algorithm is used to train and learn the labeled training samples to obtain an image classifier .
  • the classifier training module 23 needs to label each image block in order to distinguish different image blocks, that is, image blocks containing text information and image blocks that do not contain text information. For example, an image block containing text information is marked with 1 and an image block without text information is marked with 0.
  • the deep learning classification algorithms include, but are not limited to, any of the following: Naive Bayes algorithm, artificial neural network algorithm, genetic algorithm, K-Nearest Neighbor (KNN) classification algorithm, clustering algorithm, and the like.
  • KNN K-Nearest Neighbor
  • the image classifier obtained through this embodiment not only has an automatic block function, but also can directly determine whether each image block contains text information.
  • the picture blocking module 21 is specifically configured to: input a picture to be detected into an image classifier, and divide the picture to be detected by the image classifier to obtain at least one image block;
  • the device of this embodiment further includes a text detection module 24; wherein the text detection module 24 is configured to perform text detection on the image block through the image classifier, and determine the text detection result of the image block according to the classification result of the image classifier.
  • the text detection module 24 includes: a scoring unit 241 and a text detection unit 242; wherein the scoring unit 241 is configured to score each image block through an image classifier to obtain a score value of each image block; the text detection unit 242 is configured to: Determine the text detection result of the image block according to the score.
  • the score may be a normalized score, for example, any value from 0 to 100 or 0-1.
  • the character detection unit 242 is specifically configured to: if the score exceeds a preset score, determine that the image block contains text information; or select a maximum score from the scores, and if the maximum score exceeds the preset score, Determine that the image block contains text information; or, if the score is less than a preset score, determine that the image block contains text information; or select a minimum score from the scores, and if the minimum score is less than the preset score, It is determined that the image block contains text information.
  • a scoring rule can be set in advance. For example, the larger the score, the higher the probability that the character information is included, or the smaller the score, the higher the probability that the character information is included. Based on the scoring rules set above, it is determined whether the image block contains text information.
  • the text detection module 24 is specifically configured to: perform text detection on each image block through an image classifier, and directly output any of the following results through the image classifier: including text information and not including text information; and using the output result as an image Block text detection results.
  • FIG. 3 is a hardware block diagram illustrating a video text detection hardware device according to an embodiment of the present disclosure.
  • a video text detection hardware device 30 according to an embodiment of the present disclosure includes a memory 31 and a processor 32.
  • the memory 31 is configured to store non-transitory computer-readable instructions.
  • the memory 31 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory.
  • the volatile memory may include, for example, a random access memory (RAM) and / or a cache memory.
  • the non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, and the like.
  • the processor 32 may be a central processing unit (CPU) or other form of processing unit having data processing capabilities and / or instruction execution capabilities, and may control other components in the video text detection hardware device 30 to perform a desired function.
  • the processor 32 is configured to run the computer-readable instructions stored in the memory 31, so that the video text detection hardware device 30 executes the foregoing video text detection method of the embodiments of the present disclosure. All or part of the steps.
  • this embodiment may also include well-known structures such as a communication bus and an interface. These well-known structures should also be included in the protection scope of the present disclosure. within.
  • FIG. 4 is a schematic diagram illustrating a computer-readable storage medium according to an embodiment of the present disclosure.
  • a computer-readable storage medium 40 according to an embodiment of the present disclosure stores non-transitory computer-readable instructions 41 thereon.
  • the non-transitory computer-readable instruction 41 is executed by a processor, all or part of the steps of the method for comparing video features of the foregoing embodiments of the present disclosure are performed.
  • the computer-readable storage medium 40 includes, but is not limited to, optical storage media (for example, CD-ROM and DVD), magneto-optical storage media (for example, MO), magnetic storage media (for example, magnetic tape or mobile hard disk), Non-volatile memory rewritable media (for example: memory card) and media with built-in ROM (for example: ROM box).
  • optical storage media for example, CD-ROM and DVD
  • magneto-optical storage media for example, MO
  • magnetic storage media for example, magnetic tape or mobile hard disk
  • Non-volatile memory rewritable media for example: memory card
  • media with built-in ROM for example: ROM box
  • FIG. 5 is a schematic diagram illustrating a hardware structure of a terminal according to an embodiment of the present disclosure. As shown in FIG. 5, the video text detection terminal 50 includes the foregoing video text detection device embodiment.
  • the terminal may be implemented in various forms, and the terminal in the present disclosure may include, but is not limited to, such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP ( Portable multimedia players), navigation devices, on-board terminals, on-board display terminals, on-board electronic rear-view mirrors, and other mobile terminals, and fixed terminals such as digital TVs, desktop computers, and the like.
  • a mobile phone such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP ( Portable multimedia players), navigation devices, on-board terminals, on-board display terminals, on-board electronic rear-view mirrors, and other mobile terminals, and fixed terminals such as digital TVs, desktop computers, and the like.
  • PDA personal digital assistant
  • PAD tablet computer
  • PMP Portable multimedia players
  • navigation devices
  • the terminal may further include other components.
  • the video text detection terminal 50 may include a power supply unit 51, a wireless communication unit 52, an A / V (audio / video) input unit 53, a user input unit 54, a sensing unit 55, an interface unit 56, and a control unit.
  • FIG. 5 shows a terminal with various components, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead.
  • the wireless communication unit 52 allows radio communication between the terminal 50 and a wireless communication system or network.
  • the A / V input unit 53 is used to receive audio or video signals.
  • the user input unit 54 may generate key input data according to a command input by the user to control various operations of the terminal.
  • the sensing unit 55 detects the current state of the terminal 50, the position of the terminal 50, the presence or absence of a user's touch input to the terminal 50, the orientation of the terminal 50, the acceleration or deceleration movement and direction of the terminal 50, and the like, and generates a signal for controlling the terminal 50 commands or signals for operation.
  • the interface unit 56 functions as an interface through which at least one external device can be connected to the terminal 50.
  • the output unit 58 is configured to provide an output signal in a visual, audio, and / or tactile manner.
  • the memory 59 may store software programs and the like for processing and control operations performed by the controller 55, or may temporarily store data that has been output or is to be output.
  • the memory 59 may include at least one type of storage medium.
  • the terminal 50 may cooperate with a network storage device that performs a storage function of the memory 59 through a network connection.
  • the controller 57 generally controls the overall operation of the terminal.
  • the controller 57 may include a multimedia module for reproducing or playing back multimedia data.
  • the controller 57 may perform a pattern recognition process to recognize a handwriting input or a picture drawing input performed on the touch screen as characters or images.
  • the power supply unit 51 receives external power or internal power under the control of the controller 57 and provides appropriate power required to operate each element and component.
  • Various embodiments of the video feature comparison method proposed by the present disclosure may be implemented in a computer-readable medium using, for example, computer software, hardware, or any combination thereof.
  • various embodiments of the video feature comparison method proposed in the present disclosure can be implemented by using an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), and a programmable logic device. (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, electronic unit designed to perform the functions described herein, and in some cases implemented
  • ASIC application-specific integrated circuit
  • DSP digital signal processor
  • DSPD digital signal processing device
  • PLD programmable logic device
  • FPGA field programmable gate array
  • processor controller
  • microcontroller microprocessor
  • electronic unit designed to perform the functions described herein and in some cases implemented
  • Various embodiments of the video feature comparison method proposed in the present disclosure may be implemented in the controller 57.
  • various embodiments of the video feature comparison method proposed by the present disclosure can be implemented with a separate software module that allows at least one function or operation to be performed.
  • the software codes may be implemented by a software application (or program) written in any suitable programming language, and the software codes may be stored in the memory 59 and executed by the controller 57.
  • an "or” used in an enumeration of items beginning with “at least one” indicates a separate enumeration such that, for example, an "at least one of A, B, or C” enumeration means A or B or C, or AB or AC or BC, or ABC (ie A and B and C).
  • the word "exemplary” does not mean that the described example is preferred or better than other examples.
  • each component or each step can be disassembled and / or recombined.

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Abstract

La présente invention concerne un procédé de détection de texte vidéo, un dispositif de détection de texte vidéo, un dispositif matériel de détection de texte vidéo, et un support d'informations lisible par ordinateur. Le procédé de détection de texte vidéo comprend les étapes suivantes : sectionner une image à détecter extraite d'une vidéo à détecter, de façon à obtenir au moins un bloc d'image ; et déterminer si ladite vidéo comprend des informations de texte en fonction des résultats de détection de texte des blocs d'image. Dans des modes de réalisation de la présente invention, en premier lieu, l'image à détecter extraite de la vidéo à détecter est sectionnée pour obtenir au moins un bloc d'image, puis, il est déterminé si ladite vidéo comprend des informations de texte selon les résultats de détection de texte des blocs d'image ; de cette manière, la précision de détection de texte peut être améliorée.
PCT/CN2018/117715 2018-09-13 2018-11-27 Procédé et dispositif de détection de texte vidéo et support d'informations lisible par ordinateur WO2020052085A1 (fr)

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CN111753133A (zh) * 2020-06-11 2020-10-09 北京小米松果电子有限公司 视频分类方法、装置及存储介质
CN111832082A (zh) * 2020-08-20 2020-10-27 支付宝(杭州)信息技术有限公司 图文完整性检测方法及装置

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