A 4-Corner Codes Classifier Based on Decision Tree Inductive Learning for Handwritten Chinese Characters

The classification for handwritten Chinese character recognition can be viewed as a transformation in discrete vector space. In this paper, from the point of discrete vector space transformation, a new 4-corner codes classifier based on decision tree inductive learning algorithm ID3 for handwritten Chinese characters is presented. With a feature extraction controller, the classifier can reduce the number of extracted features and accelerate classification speed. Experimental results show that the 4-corner codes classifier performs well on both recognition accuracy and speed.

An on-line free handwritten Chinese character recognition method based on component cascaded HMMs

This paper presents a cascaded Hidden Markov Model （HMM）, which allows state＇s transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character （HCC） and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used tor training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples, Compared with the Segment HMMs approach, the recognition rate of this model tier the tirst candidate is 87.89% and the error rate could be reduced by 12.4%.

Data Pre－processing and Stroke Segment Extraction for On－line Handwritten Chinese Character Recognition

The stroke segments:' are proposed to be used as the basic features for handwritten Chinese character recognition. In this way, it is possible to overcome the difFiculties of unstable stroke information caused by stroke Joinings. The techniques of data pre-processing and stroke segment extraction have been described. In extracting stroke segment, not only the characteristics of the stroke itself, but also its absolute positions as well as relative positions with other strokes in the character have been taken into account.The primitive features for recognition were extracted under these comprehensive considerations.

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.

Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric

In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.

Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network

Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.

Feature Extraction of Chinese Characters Based on ASM Algorithm

In Chinese Calligraphy education,the computer-based evaluation on Chinese handwriting is one of the problems in the field of computer intelligent education.In this study,the method of feature comparison is first proposed in the process of computer-based evaluation on Chinese handwriting,focusing on automatically and accurately extracting the features of Chinese characters.Then,the key technologies applied in feature extraction of Chinese character were analyzed.It discussed the representation of features,aligns training samples,and reduces dimensions by principal component analysis,established local grayscale model,and converged the gray-scale information of target feature points through statistical analysis.The experimental results show that the accuracy of the algorithm is 93.84%.

An integration approach to handwritten Chinese character recognition system

A network integration method suitable for Chinese character recognition which combines traditional statistical method and artificial neural network is proposed to deal with the problems in machine recognition of handwritten Chinese characters which have the properties of large vocabulary, complex structure, lots of similar characters and variations of character shape due to handwriting. Four different classifiers for handwritten Chinese character recognition are integrated by the proposed method. The experimental results show that the method has a fast learning speed as well as high accuracy and can greatly improve the system performance.

Parallel compact integration in handwritten Chinese character recognition

In this paper, a new parallel compact integration scheme based on multi-layer perceptron (MLP) networks is proposed to solve handwritten Chinese character recognition (HCCR) problems. The idea of metasynthesis is applied to HCCR, and compact MLP network classifier is defined. Human intelligence and computer capabilities are combined together effectively through a procedure of two-step supervised learning. Compared with previous integration schemes, this scheme is characterized with parallel compact structure and better performance. It provides a promising way for applying MLP to large vocabulary classification.

A New Linguistic Decoding Method for Online Handwritten Chinese Character Recognition

This paper presents a new linguistic decoding method for online handwritten Chinese character recognition. The method employs a hybrid language model which combines N-gram and linguistic rules by rule quantification technique. The linguistic decoding algorithm consists of three stages: word lattice construction, the optimal sentence hypothesis search and self-adaptive learning mechanism. The technique has been applied to palmtop computer's online handwritten Chinese character recognition. Samples containing millions of characters were used to test the linguistic decoder. In the open experiment, accuracy rate up to 92% is achieved, and the error rate is reduced by 68%.

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

基于Swin Transformer和CNN的汉字书法教学系统

针对日益增长的汉字书法学习需求,将滑动窗口自注意力(Swin Transformer,ST)模型和卷积神经网络(Convolutional Neural Network,CNN)模型相结合,提出手写体汉字识别ST-CNN模型,进而开发了汉字书法教学系统。实测结果表明,ST-CNN模型识别准确率约为91.6%,较传统的ST模型提升了约0.5个百分点,较传统的CNN模型与ST模型,在收敛速度上分别提升了约10和30个百分点,开发的汉字书法教学系统性能良好。

MBRNet:融合残差连接的多分支手写字符识别网络

脱机手写中文字符识别(handwritten Chinese character recognition,HCCR)在计算机视觉领域一直是一个巨大的挑战。相比传统方法,基于深度学习的网络通过训练大量数据在识别任务中取得了差异化的效果,但识别效果依旧处于发展过程中。基于此,结合DW卷积和残差连接设计了一种多分支残差模块,该模块通过DW卷积以较小的内存和参数量为代价来加深网络深度,增强网络的特征提取能力;再通过残差连接抑制网络梯度问题和退化问题;另外,提出了一种多分支权重算法,来改善多分支残差模块中各分支的权重分配问题;并将六个以多分支残差模块为主的结构线性连接,组成HCCR识别网络。该模型在CASIA-HWDB1.0、CASIA-HWDB1.1、ICDAR2013数据集上的识别准确率分别达到了97.77%、97.30%、97.64%,表现出高精度的识别效果。

汉字识别中图特征提取方法

为解决图像像素表示汉字特征方法不能有效表示汉字本质特征、空间复杂度较高的问题,提出了一种汉字图特征提取方法。方法主要包含汉字图像二值化,汉字图像骨架提取,汉字图特征提取3个部分;二值化消除图像中的噪声,提高图特征提取的准确度;骨架提取保留图像中重要的像素点,剔除无关的像素点;图特征提取将汉字关键点与图数据结构结合来表示汉字形状特征。在3 908个常用汉字的5种字体上进行实验。结果表明,该方法能够正确提取笔画复杂汉字的图特征,有效表示汉字本质特征;不同字体汉字图特征相同的汉字数量最高为3 195个,方法表现较稳定;平均每个汉字可以用22.6个图节点、19.1个边表示,相较于用单通道图像表示汉字特征,可大幅降低空间复杂度。

一种基于多维表示的汉字识别方案

针对复杂场景下汉字特征提取难的问题,提出了一种基于多维特征表示的汉字识别方案。首先,提出一种融合空间信息的关键笔形特征提取方法,能够利用少量关键特征实现汉字的唯一识别;其次,通过多任务网络提取多维特征,增强特征提取能力,从而提高汉字识别的准确性;最后,应用字符相似度算法消除噪声,优化识别结果。实验结果表明,相较于可插拔的部首感知分支(PRAB)模型,本方案在场景数据集、网页数据集、文本数据集和手写数据集中的性能分别提升了1.62百分点、1.09百分点、0.15百分点和1.27百分点,证明了该方案的有效性。

基于关系触发词与多特征的中文人物关系抽取

针对当前主流的中文人物关系抽取方法未充分利用核心词,且难以提取中文深层文本信息的问题,提出一种基于关系触发词与多特征的中文人物关系抽取方法。将词语语义与其位置、词性、依存句法以及语义角色融合,使用结构简洁但特征提取能力更强的Transformer编码器对原始文本进行编码,基于同义词词林与词向量提取人物关系触发词,并将其作为注意力导向引入注意力机制中,提高模型对文本重要信息的学习能力。实验结果表明,该方法的F1值为89.7%,相比CNN、BiLSTM-ATT、R-BERT等模型平均提升了9.6个百分点,验证了该方法的有效性。

图像处理与卷积神经网络相结合的脱机手写汉字识别方法

为了解决传统脱机手写汉字识别方法对相似手写汉字识别率低的问题,提出了一种图像处理与卷积神经网络相结合的两阶段脱机手写汉字识别方法:第一阶段使用传统的卷积神经网络进行识别,第二阶段使用基于图像处理的差异辨别方法进行更加精确的二次识别。试验结果表明:将基于图像处理的差异辨别方法与卷积神经网络结合起来比单纯基于卷积神经网络的识别方法能够更好地识别相似汉字,从而可以提高总体手写汉字的识别率。此外,所提出的方法还表现出更稳定的识别效果,可以有效应对训练样本中存在错误标注的情况。

AN AUTOMATIC PRINTED CHINESE CHARACTER RECOGNITION SYSTEM ON MICROCOMPUTER

Based on the feature-point method of recognizing printed Chinses characters, anautomatic printed Chinese character recognition system on microcomputers is proposed. It isan entire system including layout decision, text recognition and post-editing processing.Experiments on 2 million Chinese characters indicate that this system is able to recognizeprinted Chinese characters on books, magazines and documents at a speed of 20 charachersper second on 20 MHz COMPAQ 386 and with a correct recognition rate above 95%.

脱机手写体汉字识别综述

脱机手写体汉字识别是模式识别领域最具挑战性的课题之一.本文分析了近年来脱机手写体汉字识别的最新进展,讨论了脱机手写体汉字分割、特征提取和分类器设计等关键技术的各种主流方法,介绍了3种典型的汉字识别数据库,并提出了脱机手写体汉字识别的难点问题和今后发展的趋势,为该领域的研究者指明研究方向,共同促进脱机手写体汉字识别技术的发展.

一种组合特征抽取的新方法

该文提出了一种基于特征级融合的特征抽取新方法 .首先 ,给出了一种合理的特征融合策略 ,即利用复向量给出组合特征的表示 ,将特征空间从实向量空间拓广到复向量空间 .然后 ,发展了具有统计不相关性的鉴别分析的理论 ,并将其用于复向量空间内最优鉴别特征的抽取 .最后 ,在 Concordia大学的 CENPARMI手写体阿拉伯数字数据库以及南京理工大学 NUST6 0 3HW手写汉字库上的试验结果表明 ,所提出的组合特征抽取方法不仅具有很强的维数压缩能力 。