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WO2023134402A1 - Procédé de reconnaissance de caractère de calligraphie basé sur un réseau neuronal à convolution siamois - Google Patents

Procédé de reconnaissance de caractère de calligraphie basé sur un réseau neuronal à convolution siamois Download PDF

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Publication number
WO2023134402A1
WO2023134402A1 PCT/CN2022/140065 CN2022140065W WO2023134402A1 WO 2023134402 A1 WO2023134402 A1 WO 2023134402A1 CN 2022140065 W CN2022140065 W CN 2022140065W WO 2023134402 A1 WO2023134402 A1 WO 2023134402A1
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neural network
convolutional neural
layer
similarity
calligraphy
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Chinese (zh)
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冯伟
欧宇浩
周昭坤
车其姝
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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
    • G06V30/22—Character recognition characterised by the type of writing
    • G06V30/226—Character recognition characterised by the type of writing of cursive writing

Definitions

  • the present invention relates to the technical field of calligraphy character recognition, and more specifically, to a method for recognizing calligraphy characters based on twin convolutional neural networks.
  • Calligraphy fonts can usually be divided into five categories: "Kai, Cao, Xing, Li, and Seal".
  • the morphological characteristics of different fonts are quite different, and it may be difficult for ordinary people who have not studied systematically to recognize them.
  • the handwritten continuous character " ⁇ " is easily judged as the character "Guo”.
  • the fundamental reason is that the existing recognition technology is only based on a simple feature comparison. Hundreds of pieces of data, after the user enters a word of information, through feature comparison, find the most matching result. This method requires extremely large data samples to improve the accuracy rate, but the samples of Chinese calligraphy characters are very small, so the accuracy rate of this recognition method is low, and the cost is too high.
  • solutions for calligraphy character recognition are generally divided into two categories.
  • One is not to use neural network training, but to collect samples to build a large database, then search and compare the text to be recognized in the database, and take the one with the highest similarity as the recognition result.
  • the second is to learn through the neural network. This method needs to collect a large number of sample data for training, and select the results that match the representation, so as to achieve the effect of accurate recognition.
  • patent application publication number CN103093240A extracts feature information after binarization, denoising and normalization processing of calligraphy characters, such as four boundary point positions, The average stroke crossing number, projection value, contour points, etc., and then extract the feature information of the calligraphy characters to be recognized, and then perform shape matching and comparison to give the recognition result.
  • This method has a low recognition accuracy.
  • the patent application publication number CN101785030A uses a Markov model to generate handwritten characters.
  • the trained Hidden Markov Model can use techniques such as maximum a posteriori techniques and maximum likelihood linearity, but this method also has the problem of low recognition accuracy.
  • the accuracy rate of existing calligraphy character recognition methods is not high, mainly because of the various forms of calligraphy characters and the large space for individual calligraphers to develop.
  • manual programming algorithms for traditional feature extraction The recognition effect is not ideal; the sample size of some rare characters is small, so the database that can be used for machine learning is small, which leads to the unsatisfactory training effect of traditional machine vision algorithms based on deep learning.
  • the purpose of the present invention is to overcome the defective of above-mentioned prior art, a kind of calligraphy word recognition method based on Siamese convolutional neural network is provided.
  • the method includes the following steps:
  • the calligraphic word picture is input into the twin convolutional neural network model through training, and this twinned neural network model comprises the first convolutional neural network and the second convolutional neural network, wherein the first convolutional neural network outputs the corresponding first feature Vector, the second feature vector corresponding to the output of the second convolutional neural network;
  • the category of the calligraphy character is predicted based on the similarity result.
  • the present invention has the advantage of being able to complete learning (few-/one-shot learning) with a small number of samples or even a single sample, thereby significantly reducing the amount of neural network training without losing accuracy.
  • Neural networks can be successfully used for calligraphy character recognition.
  • traditional deep learning methods based on convolutional neural networks cannot recognize objects that have not been encountered in training. If the neural network needs to recognize new objects, it is necessary to collect a large number of samples of the object, and the entire neural network (or At least the fully connected layers of the neural network) for retraining.
  • the Siamese neural network architecture provided by the present invention does not directly output the label of the sample, but outputs the similarity value between the sample and other members in the sample library.
  • Fig. 1 is the flow chart of the calligraphy word recognition method based on twin convolutional neural network according to one embodiment of the present invention
  • FIG. 2 is an overall architecture diagram of a twin convolutional neural network according to an embodiment of the present invention.
  • FIG. 3 is an overall architecture diagram of a twin convolutional neural network according to another embodiment of the present invention.
  • FIG. 4 is a specific structural diagram of a twin convolutional neural network according to an embodiment of the present invention.
  • Fig. 5 is a schematic diagram of a font sample according to an embodiment of the present invention.
  • Fig. 6 is a comparison diagram of experimental effects according to an embodiment of the present invention.
  • Input Layer-input layer input-input; output-output; none-none; Model-model; Functional-functionality; Euclidean Distance-Euclidean distance; Max Pooling-maximum pooling; Global Average Pooling - Global average pooling.
  • the present invention builds a model framework based on twin convolutional neural networks to realize calligraphy character recognition.
  • the two samples in the training set are respectively input into two identical convolutional neural networks to obtain two feature vectors.
  • the difference between the Boolean value of the reverse conduction label and the calculated similarity value is performed and stochastic gradient descent is performed to train the neural network.
  • the present invention can be used to recognize calligraphy characters, and can also be used to recognize the fonts of calligraphy characters, such as regular script, cursive script, running script and the like.
  • the provided calligraphy character recognition method based on twin convolutional neural network includes the following steps.
  • Step S110 constructing a Siamese convolutional neural network model.
  • the overall architecture of the Siamese convolutional neural network includes an input layer, two convolutional neural networks, a pooling layer (marked as dense_1) and a fully connected layer (marked as dense_2).
  • the processing process of the twin convolutional neural network is: receive two grayscale images of the same size, such as 100 ⁇ 100, and input the images into two identical deep convolutional neural networks (CNN) to extract features of different depths.
  • CNN deep convolutional neural networks
  • each convolutional neural network contains four levels of feature extraction structures, and each feature extraction structure mainly includes convolutional layers and pooling layers, see Table 1 below. Images are first sent to convolutional layers, followed by pooling layers. Then, apply the ReLU activation function and batch normalization (BN, Batchnomalization).
  • the structure of the Siamese convolutional neural network is shown in Figure 3 and Figure 4, wherein m and n are an integer between 28 and 1000, and x is between 10 and 100 an integer.
  • the first feature extraction structure is set as:
  • 32-128 convolution kernels are p ⁇ p matrices, where p is an integer between 5 and 15;
  • a k ⁇ k pooling layer where k is an integer between 1 and 5;
  • the dropout layer retains 25% to 75% of the number of neurons.
  • the second feature extraction structure is set as:
  • 64-256 convolution kernels are q ⁇ q matrices, where q is an integer between 5 and 10;
  • a k ⁇ k pooling layer where k is an integer between 1 and 5;
  • the dropout layer retains 25% to 75% of the number of neurons.
  • the third feature extraction structure is set as:
  • 64-256 convolution kernels are s ⁇ s matrices, where s is an integer between 2 and 6;
  • a k ⁇ k pooling layer where k is an integer between 1 and 5;
  • the dropout layer retains 25% to 75% of the number of neurons.
  • the fourth feature extraction structure is set as:
  • 128-512 convolution kernels are t ⁇ t matrices, where t is an integer between 2 and 6;
  • a k ⁇ k pooling layer where k is an integer between 1 and 5;
  • the dropout layer retains 25% to 75% of the number of neurons.
  • Step S120 collect data sets, and build a training set to train the Siamese neural network model, the training set reflects the correspondence between words or fonts and sample pictures.
  • this step at first collect the data set, and then construct the training set, in one embodiment, this training set comprises a plurality of words (namely with word as category), and each word corresponds to one or more samples, wherein each word The corresponding samples reflect different font classes and different morphological characteristics.
  • Chinese calligraphy characters can be downloaded from http://www.shufazidian.com/ website, as of July 23, 2021, the website has stored a total of 440,412 images, including 8 fonts and 6197 different characters. For commonly used characters, the number of corresponding fonts is more, and some font samples have few or no samples. Table 2 is a summary of word counts and the number of samples per word.
  • Figure 5 illustrates an example word containing 38 samples and an image containing multiple words.
  • images with multiple words are deleted if the word already has three or more samples. For categories with fewer than three samples, images with multiple words are kept and separated into individual fonts.
  • Preprocessing of input images includes organization of image files, normalization of image shape and color, normalization of image resolution, and creation of training and test sets. Considering that the resolution and color of different images are very different, the resolution is too low to cause information loss, and the resolution is too high to cause insufficient memory, preferably, 100 ⁇ 100 pixels are used. Since color usually does not play a role in calligraphy recognition, all images can be converted to grayscale. Then, the pixel values are normalized to a range of 0-1, and the pixel values are normalized to have a mean and unit variance of zero.
  • the twin convolutional neural network model provided by the present invention can realize the recognition of word categories or font categories.
  • the recognition of fonts and fonts is trained separately for simpler design and more direct coding.
  • the training time is shorter.
  • all fonts belonging to each character are merged, and then the samples in each font class are randomly divided into training set, validation set and test set in the ratio of 8:1:1.
  • all fonts belonging to each font are combined, and then the samples in each font category are randomly divided into training set and verification set in a ratio of 8:1:1 and the test set.
  • the dataset is not subjected to noise removal, contrast enhancement, extraneous object removal, etc. Because the convolutional neural network used will automatically take these factors into account. In addition, in order to reduce the amount of sample data, the data diversity is increased by random rotation and/or displacement of samples.
  • the number of objects collected is more than 3000, and the number of samples for each object is greater than or equal to 1.
  • the specific method is to randomly delete part of the members in the sample set of words whose number of samples is greater than 10, so that the final number of samples is less than 10. This can ensure that the trained twin convolutional neural network does not rely on large sample data sets. In the process of using after learning, if new words are encountered, it can be expanded efficiently without collecting a large number of new word samples for training. .
  • an abridged version of the data set is used.
  • the reason for training the small-sample calligraphic character recognition model is the small number of some character samples and the need to be able to recognize new character categories that are not included in the dataset of 6197 Chinese calligraphy characters.
  • the samples of each word are randomly deleted so that there are no more than 3 samples per word. Then, repeat the training, validation, and test set separation process above to create datasets for word and font recognition, respectively. Table 3 shows the word count and sample count statistics after the training set has been reduced.
  • the training process of the Siamese convolutional neural network is shown in Figure 2 and Figure 3.
  • Sample A and sample B with a resolution of m ⁇ n are respectively input into two identical Convolutional neural network.
  • the pictures of the two input characters are respectively calculated by the convolutional neural network, and after obtaining two 10- to 100-dimensional feature vectors of a single word, the two vectors are calculated by Euclidean distance or cosine similarity.
  • the Euclidean distance of the two output feature vectors is smaller or the cosine similarity is larger; if the output A and B are not the same word, the two output feature vectors The Euclidean distance of the eigenvectors is larger or the cosine similarity is smaller.
  • the similarity difference is used as a loss function for back propagation (back propagation), which can update all the weights and biases of the entire Siamese neural network architecture, thereby completing the training.
  • Step S130 using the target picture containing calligraphy characters as input, using the trained Siamese neural network model to predict the character category or font category.
  • the target image can be recognized in real time. For example, for the category of a picture to be predicted, the same number of pictures can be extracted from different categories, and then input into the twin neural network for prediction with this picture to be predicted, and obtained by calculating which one is similar to the image of different categories forecast result.
  • the present invention uses the twin convolutional neural network architecture to complete the training with small sample size data, and achieve high recognition accuracy.
  • it when encountering a word that does not exist in the training set, it will not be misclassified, but it will be recognized as a word that has not been seen before, and it can be recognized after seeing it only once.
  • the present invention can be a system, method and/or computer program product.
  • a computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to implement various aspects of the present invention.
  • a computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device.
  • a computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded device, such as a printer with instructions stored thereon A hole card or a raised structure in a groove, and any suitable combination of the above.
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disc read only memory
  • DVD digital versatile disc
  • memory stick floppy disk
  • mechanically encoded device such as a printer with instructions stored thereon
  • a hole card or a raised structure in a groove and any suitable combination of the above.
  • computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.
  • Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or downloaded to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or a network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
  • Computer program instructions for carrying out operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or Source or object code written in any combination, including object-oriented programming languages—such as Smalltalk, C++, Python, etc., and conventional procedural programming languages—such as the “C” language or similar programming languages.
  • Computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as via the Internet using an Internet service provider). connect).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, field programmable gate array (FPGA), or programmable logic array (PLA)
  • FPGA field programmable gate array
  • PDA programmable logic array
  • These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that when executed by the processor of the computer or other programmable data processing apparatus , producing an apparatus for realizing the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
  • These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause computers, programmable data processing devices and/or other devices to work in a specific way, so that the computer-readable medium storing instructions includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks in flowcharts and/or block diagrams.
  • each block in a flowchart or block diagram may represent a module, a portion of a program segment, or an instruction that includes one or more Executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by means of hardware, implementation by means of software, and implementation by a combination of software and hardware are all equivalent.

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Abstract

L'invention concerne un procédé de reconnaissance de caractère de calligraphie basé sur un réseau neuronal convolutif siamois. Le procédé consiste à : obtenir une image de caractère de calligraphie à reconnaître ; entrer l'image de caractère de calligraphie dans un modèle de réseau neuronal à convolution siamois entraîné, le modèle de réseau neuronal siamois comprenant un premier réseau neuronal à convolution et un second réseau neuronal à convolution, le premier réseau neuronal à convolution délivrant un premier vecteur de caractéristique correspondant, et le second réseau neuronal à convolution délivrant un second vecteur de caractéristique correspondant ; calculer la similarité entre le premier vecteur de caractéristique et le second vecteur de caractéristique ; et prédire la catégorie d'un caractère de calligraphie sur la base du résultat de similarité. Selon le procédé, le réseau neuronal à convolution siamois peut être entraîné au moyen d'un petit nombre d'échantillons ou même d'un seul échantillon, ce qui permet de réduire le coût d'apprentissage et d'améliorer de manière significative la précision de la reconnaissance de caractères calligraphiques.
PCT/CN2022/140065 2022-01-14 2022-12-19 Procédé de reconnaissance de caractère de calligraphie basé sur un réseau neuronal à convolution siamois Ceased WO2023134402A1 (fr)

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CN117173718A (zh) * 2023-09-19 2023-12-05 百望股份有限公司 一种智能化的字体匹配方法
CN117437530A (zh) * 2023-10-12 2024-01-23 中国科学院声学研究所 合成孔径声纳感兴趣小目标孪生匹配识别方法及系统
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