Handwritten Chinese Character Research Articles

Chinese Character Recognition Using Non-negative Matrix Factorization

Non-negative matrix factorization (NMF) was introduced by Paatero and Tapper in 1994 and it was a general way of reducing the dimension of the matrix with non-negative entries. Non-negative matrix factorization is very useful in many data analysis applications such as character recognition, text mining, and others. This paper aims to study the application in Chinese character recognition using non-negative matrix factorization. Python was used to carry out the LU factorization and non-negative matrix factorization of a Chinese character in Boolean Matrix. Preliminary analysis confirmed that the data size of and and are chosen for the NMF of the Boolean matrix. In this project, one hundred printed Chinese characters were selected, and all the Chinese characters can be categorized into ten categories according to the number of strokes , for . The Euclidean distance between the Boolean matrix of a Chinese character and the matrix after both LU factorization and NMF is calculated for further analysis. Paired t-test confirmed that the factorization of Chinese characters in the Boolean matrix using NMF is better than the LU factorization. Finally, ten handwritten Chinese characters were selected to test whether the program is able to identify the handwritten and the printed Chinese characters. Experimental results showed that 70% of the characters can be recognized via the least Euclidean distance obtained. NMF is suitable to be applied in Chinese character recognition since it can reduce the dimension of the image and the error between the original Boolean matrix and after NMF is less than 5%.

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.

An approach for handwritten Chinese text recognition unifying character segmentation and recognition

Text line recognition methods can be categorized into explicit segmentation based and implicit segmentation based ones. Explicit segmentation based methods require character-level annotation during training, while implicit segmentation based methods, trained on line-level annotated data, face alignment drift challenges. Though some methods have been proposed to address these challenges using weakly supervised object detection, they often rely on cumbersome pseudo-box generation processes and complex decoding. In this paper, we propose a unified framework to overcome these challenges, achieving high accuracy in text recognition and character segmentation. To eliminate the need of character-level annotated real text line data in training, we introduce a novel training paradigm that utilizes character-level annotated synthetic data and line-level annotated real data jointly. For synthetic data, candidate characters are explicitly aligned with labeled characters to generate hard labels for supervising model training. For real data, implicit alignment is produced by Connectionist Temporal Classification (CTC) mapping to provide soft labels for weakly-supervised model training. And for inference, we propose two decoding strategies leveraging the advantages of Non-Maximum Suppression (NMS) and CTC decoding. Extensive experiments on benchmark datasets demonstrate the superior performance of our method in text recognition and character localization, even with minimal amounts of character-level annotated line data.

A Review of the Current Status of AI Research in Handwritten Chinese Character Recognition

With the continuous development of artificial intelligence technology, there have been attempts to utilize AI in the field of linguistics. In this context, research and development in AI-based Chinese character recognition technology has spanned 40 years, with its outcomes attracting significant attention. However, in the field of Chinese character recognition, the research into offline handwritten recognition technology is particularly challenging due to the unique characteristics of handwritten Chinese characters. The main issue in current research is the significant decrease in accuracy for characters composed of similar components or characters with similar overall shapes. The focus of research solutions has been on modern mechanical programming and other engineering aspects, while research that integrates the inherent characteristics of Chinese characters themselves has been relatively overlooked. The author believes that combining engineering technology research with a deeper understanding of the nature of Chinese characters can solve many of the current problems. This paper reviews the current state of related domestic and international research to identify areas with significant potential for improvement and progress in research, and finally proposes directions for future research.

Research on Handwritten Chinese Characters in Chinese Proficiency Grading Standards for International Chinese Language Education

Research on Handwritten Chinese Characters in Chinese Proficiency Grading Standards for International Chinese Language Education

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

Online Handwritten Chinese Character Recognition Based on 1-D Convolution and Two-Streams Transformers

Online Handwritten Chinese Character Recognition Based on 1-D Convolution and Two-Streams Transformers

Research and design of Chinese cookie model with chun character based on OpenSCAD

In todays society, the 3D modelling field has become an indispensable part of peoples lives. Whether it is the metaverse, games, industry, or medicine, 3D modelling cannot be separated from them. The models used in various fields are also diverse. In the models of various fields, there are not many models for reference in the food fields biscuit modelling and there are no special views. This article creates a Chinese New Year biscuit with the Chinese handwritten character Spring on it based on the different styles and types of biscuit models in the food industry. OpenSCAD software was used to create this model. This article first introduces the methods and usage of OpenSCAD software that are encapsulated for modelling. Then this paper describes in detail how to layer and create biscuits layer by layer. How to use code to create a handwritten Chinese character and a cream biscuit completely in OpenSCAD is detailed by this article. For the result, this article implements a Chinese New Year biscuit with a reference to the Spring character written by the ancient Chinese calligrapher Yan Zhenqing. This work fully implements every stroke of handwritten Chinese characters in the cookie model, and these details have been recognized by the majority of people in the final evaluation.

Reconstructed SqueezeNext with C-CBAM for offline handwritten Chinese character recognition.

Handwritten Chinese character recognition (HCCR) is a difficult problem in character recognition. Chinese characters are diverse and many of them are very similar. The HCCR model consumes a large number of computational resources during runtime, making it difficult to deploy to resource-limited development platforms. In order to reduce the computational consumption and improve the operational efficiency of such models, an improved lightweight HCCR model is proposed in this article. We reconstructed the basic modules of the SqueezeNext network so that the model would be compatible with the introduced attention module and model compression techniques. The proposed Cross-stage Convolutional Block Attention Module (C-CBAM) redeploys the Spatial Attention Module (SAM) and the Channel Attention Module (CAM) according to the feature map characteristics of the deep and shallow layers of the model, targeting enhanced information interaction between the deep and shallow layers. The reformulated intra-stage convolutional kernel importance assessment criterion integrates the normalization nature of the weights and allows for structured pruning in equal proportions for each stage of the model. The quantization aware training is able to map the 32-bit floating-point weights in the pruned model to 8-bit fixed-point weights with minor loss. Pruning with the new convolutional kernel importance evaluation criterion proposed in this article can achieve a pruning rate of 50.79% with little impact on the accuracy rate. The various optimization methods can compress the model to 1.06 MB and achieve an accuracy of 97.36% on the CASIA-HWDB dataset. Compared with the initial model, the volume is reduced by 87.15%, and the accuracy is improved by 1.71%. The model proposed in this article greatly reduces the running time and storage requirements of the model while maintaining accuracy.

Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods

Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics’ interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. However, the recognition accuracy of offline HCCR is still a sophisticated challenge owing to their complexity and variety of writing styles. With the advancement of deep learning, convolutional neural network (CNN)-based algorithms have demonstrated distinct benefits in offline HCCR and have achieved outstanding results. In this review, we aim to show the different HCCR methods for tackling the complexity and variability of offline HCC writing styles. This paper also reviews different activation functions used in offline HCCR and provides valuable assistance to new researchers in offline Chinese handwriting recognition by providing a succinct study of various methods for recognizing offline HCC.

Deep Learning Inferencing with High-performance Hardware Accelerators

As computer architectures continue to integrate application-specific hardware, it is critical to understand the relative performance of devices for maximum app acceleration. The goal of benchmarking suites, such as MLPerf for analyzing machine learning (ML) hardware performance, is to standardize a fair comparison of different hardware architectures. However, there are many apps that are not well represented by these standards that require different workloads, such as ML models and datasets, to achieve similar goals. Additionally, many apps, like real-time video processing, are focused on latency of computations rather than strictly on throughput. This research analyzes multiple compute architectures that feature ML-specific hardware on a case study of handwritten Chinese character recognition. Specifically, AlexNet and a custom version of GoogLeNet are benchmarked in terms of their streaming latency and maximum throughput for optical character recognition. Considering that these models are composed of fundamental neural network operations yet architecturally different from each other, these models can stress devices in different yet insightful ways that generalizations of the performance of other models can be drawn from. Many devices featuring ML-specific hardware and optimizations are analyzed including Intel and AMD CPUs, Xilinx and Intel FPGAs, NVIDIA GPUs, and Google TPUs. Overall, ML-oriented hardware added to the Intel Xeon CPUs helps to boost throughput by 3.7× and to reduce latency by up to 34.7×, which makes the latency of Intel Xeon CPUs competitive on more parallel models. The TPU devices were limited in terms of throughput due to large data transfer times and not competitive in terms of latency. The FPGA frameworks showcase the lowest latency on the Xilinx Alveo U200 FPGA achieving 0.48 ms on AlexNet using Mipsology Zebra and 0.39 ms on GoogLeNet using Vitis-AI. Through their custom acceleration datapaths coupled with high-performance SRAM, the FPGAs are able to keep critical model data closer to processing elements for lower latency. The massively parallel and high-memory GPU devices with Tensor Core accelerators achieve the best throughput. The NVIDIA Tesla A100 GPU showcases the highest throughput at 42,513 and 52,484 images/second for AlexNet and GoogLeNet, respectively. 1

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.

Particle Swarm Optimization-Based Convolutional Neural Network for Handwritten Chinese Character Recognition

Recently, handwritten Chinese character recognition has become an important research field in computer vision. With the development of deep learning, convolutional neural networks (CNNs) have demonstrated excellent performance in computer vision. However, CNNs are typically designed manually, which requires extensive experience and may lead to redundant computations. To solve these problems, in this study, the particle swarm optimization approach is incorporated into the design of a CNN for handwritten Chinese character recognition, reducing redundant computations in the network. In this approach, each network architecture is represented by a particle, and the optimal network architecture is determined by continuously updating the particles until a global particle is identified. The experimental validation resulted in a network accuracy of 97.24% with only 1.43 million network parameters. Therefore, it is demonstrated that the proposed particle swarm optimization method can quickly and accurately find the optimal network architecture.

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work.

Handwritten Multi-Scale Chinese Character Detector with Blended Region Attention Features and Light-Weighted Learning.

Character-level detection in historical manuscripts is one of the challenging and valuable tasks in the computer vision field, related directly and effectively to the recognition task. Most of the existing techniques, though promising, seem not powerful and insufficiently accurate to locate characters precisely. In this paper, we present a novel algorithm called free-candidate multiscale Chinese character detection FC-MSCCD, which is based on lateral and fusion connections between multiple feature layers, to successfully predict Chinese characters of different sizes more accurately in old documents. Moreover, cheap training is exploited using cheaper parameters by incorporating a free-candidate detection technique. A bottom-up architecture with connections and concatenations between various dimension feature maps is employed to attain high-quality information that satisfies the positioning criteria of characters, and the implementation of a proposal-free algorithm presents a computation-friendly model. Owing to a lack of handwritten Chinese character datasets from old documents, experiments on newly collected benchmark train and validate FC-MSCCD to show that the proposed detection approach outperforms roughly all other SOTA detection algorithms.

Offline handwritten Tai Le character recognition using wavelet deep convolution features and ensemble deep variationally sparse Gaussian processes

Handwriting recognition is an important application in pattern recognition. Because handwritten Tai Le has similar characters and the proportion of characters with similar shapes is relatively high, this paper proposes a wavelet deep convolution (WDC) feature combined with an ensemble deep variational sparse Gaussian process (EDVSGP) offline handwritten Tai Le recognition method. A Tai Le recognition sample database is constructed, 2 and 3 levels of wavelet decomposition prior features are extracted, and then, the wavelet a priori feature is transformed into a WDC feature. To avoid the dimensional disaster caused by feature fusion, a dimensionality reduction method combining LDA and PCA is used to reducing the dimensionality of the fused features without reducing the accuracy rate. Moreover, to better recognize handwritten Tai Le characters, 6 deep variationally sparse Gaussian process (DVSGP) models are integrated as an EDVSGP classification model. Using the handwritten Tai Le character database, the proposed method is superior to the existing methods, with an accuracy, recall and F1-score of 94.15%, 94.15%, and 94.15%, respectively. The universality of the method is verified on a dataset of handwritten Chinese characters with similar shapes (HCFC2021.4) and the Devanagari dataset.

Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition

Chinese characters carry a wealth of morphological and semantic information; therefore, zero-shot Chinese character recognition with the morphology of Chinese characters has drawn significant attention. The previous methods are mainly based on radical-level decomposition or stroke-level decomposition, which usually cannot capture adequately the structural and spatial information of Chinese characters. In this paper, we develop a stroke-based autoencoder (SAE), to model the sophisticated morphology of Chinese characters with a self-supervised method. Following its canonical writing order, we first represent a Chinese character as a series of stroke images with a fixed writing order, and then our SAE model is trained to reconstruct this stroke image sequence. This pre-trained SAE model can predict the stroke image series for unseen characters, as long as their strokes or radicals are in the training set. We have designed two contrasting SAE architectures on different forms of stroke images. One is fine-tuned on existing stroke-based method for zero-shot recognition of handwritten Chinese characters, and the other is applied to enrich the Chinese word embeddings from their morphological features. The experimental results validate that after pre-training, our SAE architecture outperforms other existing methods in zero-shot recognition and enhances the representation of Chinese characters with their abundant morphological and semantic information.

Characters as graphs: Interpretable handwritten Chinese character recognition via Pyramid Graph Transformer

It is meaningful but challenging to teach machines to recognize handwritten Chinese characters. However, conventional approaches typically view handwritten Chinese characters as either static images or temporal trajectories, which may ignore the inherent geometric semantics of characters. Instead, here we first propose to represent handwritten characters as skeleton graphs, explicitly considering the natural characteristics of characters (i.e., characters as graphs). Furthermore, we propose a novel Pyramid Graph Transformer (PyGT) to specifically process the graph-structured characters, which fully integrates the advantages of Transformers and graph convolutional networks. Specifically, our PyGT can learn better graph features through (i) capturing the global information from all nodes with graph attention mechanism and (ii) modelling the explicit local adjacency structures of nodes with graph convolutions. Furthermore, the PyGT learns the multi-resolution features by constructing a progressive shrinking pyramid. Compared with existing approaches, it is more interpretable to recognize characters as geometric graphs. Moreover, the proposed method is generic for both online and offline handwritten Chinese character recognition (HCCR), and it also can be feasibly extended to handwritten text recognition. Extensive experiments empirically demonstrate the superiority of PyGT over the prevalent approaches including 2D-CNN, RNN/1D-CNN, and Vision Transformer (ViT) for HCCR. The code is available at https://github.com/ganji15/PyGT-HCCR.

A Tree-Structure Analysis Network on Handwritten Chinese Character Error Correction

Existing researches on handwritten Chinese characters are mainly based on recognition network designed to solve the complex structure and numerous amount characteristics of Chinese characters. In this paper, we investigate Chinese characters from the perspective of error correction, which is to diagnose a handwritten character to be right or wrong and provide a feedback on error analysis. For this handwritten Chinese character error correction task, we define a benchmark by unifying both the evaluation metrics and data splits for the first time. Then we design a diagnosis system that includes decomposition, judgement and correction stages. Specifically, a novel tree-structure analysis network (TAN) is proposed to model a Chinese character as a tree layout. Using the predicted tree layout for judgement, if the character is wrongly written, correction operation is needed for error analysis. The correction stage mainly consists of three steps: fetch the ideal character, correct the errors and locate the errors. Computing the distance between the model output and all candidate characters can generate ideal characters. With the decomposition ability of TAN, edit distance computing between predicted tree and ideal tree can lead to correction operations. By using attention visualization, we can even provide the approximate error location. Through quantitative analysis, we prove that TAN captures more accurate spatial position information than regular encoder-decoder models. Additionally, we propose a novel bucketing mining strategy to apply triplet loss at radical level to alleviate feature dispersion. Experiments on handwritten character dataset demonstrate that our proposed TAN shows great superiority on all three metrics comparing with other state-of-the-art models.

A novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition

To solve the problems of large number of similar Chinese characters, difficult feature extraction and inaccurate recognition, we propose a novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition. First, we design a deep convolutional neural network model for feature grouping extraction and fusion. The multilevel stacked feature group extraction module is used to extract the deep abstract feature information of the image and carry out the fusion between the different feature information modules. Secondly, we use the designed down-sampling and channel amplification modules to reduce the feature dimension while preserving the important information of the image. The feature information is refined and condensed to solve the overlapping and redundant problem of feature information. Thirdly, inter-layer feature fusion algorithm and Softmax classification function constrained by L2 norm are used. We further compress the parameter clipping to avoid the loss of too much accuracy due to the clipping of important parameters. The dynamic network surgery algorithm is used to ensure that the important parameters of the error deletion are reassembled. Experimental results on public data show that the designed recognition model in this paper can effectively improve the recognition rate of handwritten Chinese characters.