Off-line Handwritten Chinese Character Recognition Research Articles

Increasing Offline Handwritten Chinese Character Recognition Using Separated Pre-Training Models: A Computer Vision Approach

Offline handwritten Chinese character recognition involves the application of computer vision techniques to recognize individual handwritten Chinese characters. This technology has significantly advanced the research in online handwriting recognition. Despite its widespread application across various fields, offline recognition faces numerous challenges. These challenges include the diversity of glyphs resulting from different writers’ styles and habits, the vast number of Chinese character labels, and the presence of morphological similarities among characters. To address these challenges, an optimization method based on a separated pre-training model was proposed. The method aims to enhance the accuracy and robustness of recognizing similar character images by exploring potential correlations among them. In experiments, the HWDB and Chinese Calligraphy Styles by Calligraphers datasets were employed, utilizing precision, recall, and the Macro-F1 value as evaluation metrics. We employ a convolutional self-encoder model characterized by high recognition accuracy and robust performance. The experimental results demonstrated that the separated pre-training models improved the performance of the convolutional auto-encoder model, particularly in handling error-prone characters, resulting in an approximate 6% increase in precision.

Deep Convolutional Neural Networks Based on Knowledge Distillation for Offline Handwritten Chinese Character Recognition

Deep convolutional neural networks (DNNs) have achieved outstanding performance in this field. Meanwhile, handwritten Chinese character recognition (HCCR) is a challenging area of research in the field of computer vision. DNNs require a large number of parameters and high memory consumption. To address these issues, this paper proposes an approach based on an attention mechanism and knowledge distillation. The attention mechanism improves the feature extraction and the knowledge distillation reduces the number of parameters. The experimental results show that ResNet18 achieves a recognition accuracy of 97.63% on the HCCR dataset with 11.25 million parameters. Compared with other methods, this study improves the performance for HCCR.

Research on Offline Handwritten Chinese Character Recognition Algorithm Based on Gated Convolution and Stacked Self-Attention

Research on Offline Handwritten Chinese Character Recognition Algorithm Based on Gated Convolution and Stacked Self-Attention

LW-ViT: The Lightweight Vision Transformer Model Applied in Offline Handwritten Chinese Character Recognition

In recent years, the transformer model has been widely used in computer-vision tasks and has achieved impressive results. Unfortunately, these transformer-based models have the common drawback of having many parameters and a large memory footprint, causing them to be difficult to deploy on mobiles as lightweight convolutional neural networks. To address these issues, a Vision Transformer (ViT) model, named the lightweight Vision Transformer (LW-ViT) model, is proposed to reduce the complexity of the transformer-based model. The model is applied to offline handwritten Chinese character recognition. The design of the LW-ViT model is inspired by MobileViT. The lightweight ViT model reduces the number of parameters and FLOPs by reducing the number of transformer blocks and the MV2 layer based on the overall framework of the MobileViT model. The number of parameters and FLOPs for the LW-ViT model was 0.48 million and 0.22 G, respectively, and it ultimately achieved a high recognition accuracy of 95.8% on the dataset. Furthermore, compared to the MobileViT model, the number of parameters was reduced by 53.8%, and the FLOPs were reduced by 18.5%. The experimental results show that the LW-ViT model has a low number of parameters, proving the correctness and feasibility of the proposed model.

PF-ViT: Parallel and Fast Vision Transformer for Offline Handwritten Chinese Character Recognition.

Recently, Vision Transformer (ViT) has been widely used in the field of image recognition. Unfortunately, the ViT model repeatedly stacks 12-layer encoders, resulting in a large number of model computations, many parameters, and slow training speed, making it difficult to deploy on mobile devices. In order to reduce the computational complexity of the model and improve the training speed, a parallel and fast Vision Transformer method for offline handwritten Chinese character recognition is proposed. The method adds parallel branches of the encoder module to the structure of the Vision Transformer model. Parallel modes include two-way parallel, four-way parallel, and seven-way parallel. The original picture is fed to the encoder module after flattening and linear embedding processing operations. The core step in the encoder is the multihead attention mechanism. Multihead self-attention can learn the interdependence between image sequence blocks. In addition, the use of data expansion strategies increases the diversity of data. In the two-way parallel experiment, when the model is 98.1% accurate on the dataset, the number of parameters and the number of FLOPs are 43.11 million and 4.32 G, respectively. Compared with the ViT model, whose parameters and FLOPs are 86 million and 16.8 G, respectively, the two-way parallel model has a 50.1% decrease in parameters and a 34.6% decrease in FLOPs. This method has been demonstrated to effectively reduce the computational complexity of the model while indirectly improving image recognition speed.

Research on Handwritten Chinese Character Recognition Based on BP Neural Network

The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before. Handwritten Chinese character recognition, as a hot research object in image pattern recognition, has many applications in people’s daily life, and more and more scholars are beginning to study off-line handwritten Chinese character recognition. This paper mainly studies the recognition of handwritten Chinese characters by BP (Back Propagation) neural network. Establish a handwritten Chinese character recognition model based on BP neural network, and then verify the accuracy and feasibility of the neural network through GUI (Graphical User Interface) model established by Matlab. This paper mainly includes the following aspects: Firstly, the preprocessing process of handwritten Chinese character recognition in this paper is analyzed. Among them, image preprocessing mainly includes six processes: graying, binarization, smoothing and denoising, character segmentation, histogram equalization and normalization. Secondly, through the comparative selection of feature extraction methods for handwritten Chinese characters, and through the comparative analysis of the results of three different feature extraction methods, the most suitable feature extraction method for this paper is found. Finally, it is the application of BP neural network in handwritten Chinese character recognition. The establishment, training process and parameter selection of BP neural network are described in detail. The simulation software platform chosen in this paper is Matlab, and the sample images are used to train BP neural network to verify the feasibility of Chinese character recognition. Design the GUI interface of human-computer interaction based on Matlab, show the process and results of handwritten Chinese character recognition, and analyze the experimental results.

Off-line handwritten chinese character recognition by using support vector machines

The research proposes an efficient high-accuracy SVM-based handwritten Chinese character recognition system. The 304-dimensional feature vector of a handwritten Chinese character consists of the contour direction feature, the crossing count feature, the peripheral background area feature, and the contour line length feature. The mean-vector recognition method is first used as the coarse classifier to get a small number of candidate classes for the input vector. After the preliminary multi-class SVM-based recognition method is trained by 200 instances per candidate class, the recognition rate for the test handwritten characters of 5,401 classes can achieve 98.31%, which is much higher than about 93% accuracy for the mean-vector recognition method alone. To speed up the recognition, the forward and the backward greedy two-class SVM-based recognition methods are proposed to greatly reduce the recognition time to a practical level.

Radical aggregation network for few-shot offline handwritten Chinese character recognition

Offline handwritten Chinese character recognition has attracted much interest due to its various applications. The most cutting-edge methods treat Chinese character as a whole, ignoring the structures and radicals that compose characters. To use the radical-level composition of Chinese characters and achieve few-shot/zero-shot Chinese character recognition, some methods attempt to recognize Chinese characters at the radical level; however, these methods have shown poor performance due to weak radical feature representation and the use of inflexible decoding algorithm. In this paper, a novel radical aggregation network (RAN) is proposed for few-shot/zero-shot offline handwritten Chinese character recognition. The RAN consists of three components, a radical mapping encoder (RME), a radical aggregation module (RAM), and a character analysis decoder (CAD). Experiments show that our method can effectively recognize unseen handwritten characters given few support samples, while maintaining a high performance on seen characters.

Handwritten Chinese character recognition using fuzzy image alignment

The task of handwritten Chinese character recognition is one of the most challenging areas of human handwriting classification. The main reason for this is related to the writing system itself which encompasses thousands of characters, coupled with high levels of diversity in personal writing styles and attributes. Much of the existing work for both online and off-line handwritten Chinese character recognition has focused on methods which employ feature extraction and segmentation steps. The preprocessed data from these steps form the basis for the subsequent classification and recognition phases. This paper proposes an approach for handwritten Chinese character recognition and classification using only an image alignment technique and does not require the aforementioned steps. Rather than extracting features from the image, which often means building models from very large training data, the proposed method instead uses the mean image transformations as a basis for model building. The use of an image-only model means that no subjective tuning of the feature extraction is required. In addition by employing a fuzzy-entropy-based metric, the work also entails improved ability to model different types of uncertainty. The classifier is a simple distance-based nearest neighbour classification system based on template matching. The approach is applied to a publicly available real-world database of handwritten Chinese characters and demonstrates that it can achieve high classification accuracy and is robust in the presence of noise.

A Study of Off-Line Handwritten Chinese Character Recognition with Optimized Decision and Iteration Based on Rough Set and the Granular Theorem

The decision information system of off-line handwritten Chinese character recognition based on variable precision rough set is constructed in this paper. The granular variable in granular theorem is introduced, the granular entropy of feature attributes knowledge and decision attributes knowledge and relative granular entropy is defined. The attribute reduction algorithm with several features is given out. Moreover, the recognition structure which has three-tier classifier based on rough set iterated several times is designed. And it resolved the problem of high rate of rejection and coincident code with a single classifier and recognition only once effectively. The experiment results show that the method is feasible and effective.

Research of Off-Line Handwritten Chinese Character Recognition System Based on Feedback Structure

An offline handwritten Chinese character recognition system based on feedback structure is constructed. By imitating the feedback behavior in the human brain, the operating mechanism is presented. By comparing the recognition result character with the input character, four kinds of general characters recognition errors are defined. According to the analysis of the general errors, the evaluation mechanism and regulation mechanism of closed-loop based on feedback is made. Based on the trend of errors, the recognition process is adjusted to make the whole operating mechanism more reasonable. The results of simulation show that this system is efficient.

Study of Off-Line Handwritten Chinese Character Recognition Based on Dynamic Pruned Binary Tree SVMs

The Chinese off-line handwritten recognition is a multi-class classification problem. Since there exits certain relation between some classes, it is unnecessary to differentiate all types of the Chinese characters at the time of identifying one type of Chinese character. In this paper, an improved dynamic pruned binary tree SVM multi-classification algorithm is presents. Unvalued support vector is removed at each identification, then a new binary tree is reconstructed according to the features of the structure of the Chinese character, The number of the support vector machines is reduced and the identification speed is increased. Compared with different multi-class classification algorithms, the simulation results show that the method can improve the speed of identifying the classes of the Chinese characters. It is effective and accuracy high.

An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration

This paper presents a methodology for off-line handwritten Chinese character recognition based on mergence of consecutive segments of adaptive duration. The handwritten Chinese character string is partitioned into a sequence of consecutive segments, which are combined to implement dissimilarity evaluation within a sliding window whose durations are determined adaptively by the integration of shapes and context of evaluations. The average stroke width is estimated for the handwritten Chinese character string, and a set of candidate character segmentation boundaries is found by using the integration of pixel and stroke features. The final decisions on segmentation and recognition are made under minimal arithmetical mean dissimilarities. Experiments proved that the proposed approach of adaptive duration outperforms the method of fixed duration, and is very effective for the recognition of overlapped, broken, touched, loosely configured Chinese characters.

An Approach to Off-Line Handwritten Chinese Character Recognition Based on Hierarchical Radical Decomposition

Off-line recognition of handwritten Chinese characters is of considerable practical importance as well as representing a very hard pattern recognition problem. A popular approach is to decompose characters into their component or ‘primitive’ parts – most usually strokes. Here, however, we take the less usual approach of decomposing into radicals. Active shape modelling is applied and developed into active radical modelling. In training, 60 examples of each radical are represented by ‘landmark’ points, labelled semi-automatically, with radicals in different characteristic positions treated as distinctly different radicals. Principal component analysis then captures the main variation around the mean radical. In recognition, the dynamic tunnelling algorithm is incorporated with gradient descent to search for optimal shape parameters in terms of chamfer distance minimisation. Although prior landmark labelling is time-consuming and gradient descent search during recognition is computationally expensive, the method is theoretically well motivated, incorporates prior knowledge about the structure of Chinese characters in an appropriate way, and avoids problems implicit in stroke extraction. Experiments are conducted on 280,000 loosely constrained characters from 200 writers. There are 98 different categories of radical included in 1400 character categories, and approximately 590,000 radicals in total. The matching rate on this large test set is 94.2% radicals correct (writer-independent), greatly superior to existing radical approaches. Assuming character composition to be a Markov process in which up to four radicals are combined in some assumed sequential order, we can recognise complete, hierarchically composed characters using the Viterbi algorithm. This results in a character recognition rate of 92.6%.

Offline handwritten Chinese character recognition by radical decomposition

Offline handwritten Chinese character recognition is a very hard pattern-recognition problem of considerable practical importance. Two popular approaches are to extract features holistically from the character image or to decompose characters structurally into component parts---usually strokes. Here we take a novel approach, that of decomposing into radicals on the basis of image information (i.e., without first decomposing into strokes). During training, 60 examples of each radical were represented by "landmark" points, labeled semiautomatically, with radicals in different characteristic positions treated as distinctly different radicals. Kernel principal-component analysis then captured the main (nonlinear) variations around the mean radical. During the recognition, the dynamic tunneling algorithm was used to search for optimal shape parameters in terms of chamfer distance minimization. Considering character composition as a Markov process in which up to four radicals are combined in some assumed sequential order, we can recognize complete, hierarchically-composed characters by using the Viterbi algorithm. This gave a character recognition rate of 93.5% characters correct (writer-independent) on a test set of 430,800 characters from 2,154 character classes composed of 200 radical categories, which is comparable to the best reported results in the literature. Although the initial semiautomatic landmark labeling is time consuming, the decomposition approach is theoretically well-motivated and allows the different sources of variability in Chinese handwriting to be handled separately and by the most appropriate means--either learned from example data or incorporated as prior knowledge. Hence, high generalizability is obtained from small amounts of training data, and only simple prior knowledge needs to be incorporated, thus promising robust recognition performance. As such, there is very considerable potential for further development and improvement in the direction of larger character sets and less constrained writing conditions.

Off-line handwritten Chinese character recognition as a compound Bayes decision problem

A handwritten Chinese character off-line recognizer based on contextual vector quantization (CVQ) of every pixel of an unknown character image has been constructed. Each template character is represented by a codebook. When an unknown image is matched against a template character, each pixel of the image is quantized according to the associated codebook by considering not just the feature vector observed at each pixel, but those observed at its neighbors and their quantization as well. Structural information such as stroke counts observed at each pixel are captured to form a cellular feature vector. Supporting a vocabulary of 4616 simplified Chinese characters and alphanumeric and punctuation symbols, the writer-independent recognizer has an average recognition rate of 77.2 percent. Three statistical language models for postprocessing have been studied for their effectiveness in upgrading the recognition rate of the system. Among them, the CVQ-based language model is the most effective one upgrading the recognition rate by 10.4 percent on the average.