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%.

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.

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 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%.

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。

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。

基于改进卷积神经网络的手写汉字识别研究

手写汉字识别是智能信息化的重要研究领域之一,在手写输入、文件录入等相关领域至关重要。然而,由于汉字的种类繁多、结构复杂等问题,通常的深度学习方法对汉字的特征提取能力差、计算量大,汉字识别的准确度无法达到令人满意的程度。为解决上述问题,本研究提出了一种改进的深层卷积神经网络模型,在模型的结构上添加了批标准化层,在损失函数上加入了Dropout和正则化方法,在训练过程中加入了RMSprop优化器。为了证实提出的模型的有效性,在CASIA-HWDB1.1和IAHCC-UCAS2016的数据集上进行实验。对提出的模型进行了整体实验,与其他深度学习模型进行了分组对比实验,对批标准化层和RMSprop优化器进行了消融实验,全方面验证了本研究模型的高准确率和高运行速度。

笔画节点在手写体汉字识别中的作用

产生式理论认为,视觉图形的识别是对其产生过程的逆推理。汉字是笔画按正字法规则交错连接构成的象形文字,手写体汉字识别可以认为是对汉字产生过程的反向推理。基于典型的产生式模型——贝叶斯规划学习模型,汉字的产生式识别过程从识别字的笔画开始,先基于线段交点提取出节点,再枚举能产生该节点的所有笔画组合方式,从而获得汉字的产生方式。据此预测,节点数量和节点复杂度是手写汉字识别过程的重要影响因素。本研究通过三个实验考察了节点在汉字识别中的作用。结果显示,含有较多节点的汉字具有更好的识别绩效(节点数量效应),掩盖由较多笔画构成的高复杂度节点会对汉字识别产生更大干扰(节点复杂度效应)。本研究增进了对汉字识别早期过程的认识,为字形识别的产生式反向推理过程提供了证据。

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.

大数据下的基于深度神经网的相似汉字识别

针对传统相似手写汉字识别系统(SHCCR)受特征提取方法的限制,提出采用深度神经网(DNN)对相似汉字自动学习有效特征并进行识别,介绍相似字符集生成方法和针对相似汉字识别的深度神经网络的具体结构,研究对比不同的训练数据规模对识别性能的影响。实验表明,DNN能有效地进行特征学习,避免了人工设计特征的不足,与传统基于梯度特征的支持向量机(SVM)和最近邻分类器(1-NN)方法相比,识别率有较大的提高;且随着训练样本增加的同时,DNN在提高识别性能上表现得更为优秀,大数据训练对提升深度神经网络的识别率作用明显。

一种基于支持向量机的手写汉字识别方法

本文提出了一种新的基于支持向量机手写汉字识别方法 .支持向量机作为一种新的机器学习方法 ,由于其建立在结构风险最小化准则之上 ,而不是仅仅使经验风险达到最小 ,从而使得支持向量分类器具有较好的推广能力 .本文首先讨论了支持向量机的基本原理 ,然后 ,针对支持向量机识别大类别手写汉字所遇到的特殊问题 ,文章进行了分析和阐述 ,并在此基础上 ,提出了基于最小距离分类器预分类的两级分类策略 .最后 ,针对GB2 312 80的 10 34个汉字类别的 12 0套手写样本 ,进行了实验仿真 .实验结果表明 ,本文方法的汉字识别率较距离分类器有较大提高 ,其中多项式核函数的支持向量分类器 ,识别率平均提高 3 38% 。

深度学习在手写汉字识别中的应用综述

手写汉字识别(Handwritten Chinese character recognition,HCCR)是模式识别的一个重要研究领域,最近几十年来得到了广泛的研究与关注,随着深度学习新技术的出现,近年来基于深度学习的手写汉字识别在方法和性能上得到了突破性的进展.本文综述了深度学习在手写汉字识别领域的研究进展及具体应用.首先介绍了手写汉字识别的研究背景与现状.其次简要概述了深度学习的几种典型结构模型并介绍了一些主流的开源工具,在此基础上详细综述了基于深度学习的联机和脱机手写汉字识别的方法,阐述了相关方法的原理、技术细节、性能指标等现状情况,最后进行了分析与总结,指出了手写汉字识别领域仍需要解决的问题及未来的研究方向.

基于遗传算法的手写体汉字识别系统优化方法的研究

为了改善手写体汉字识别系统的性能 ,提出了前端单字识别器 (SCR)和后端语言解码器 (post- processingsystem )有效结合的模型 ,并且利用遗传算法对系统参数进行优化 .以联机手写体汉字识别系统作为 SCR进行测试 ,首选准确率为 6 9.46 % ,汉字识别的准确率达到 87.5 9% ,较优化前提高 6 .4% .实验结果表明 ,遗传算法 (GA )是一种有效的优化系统参数的方法 .

基于弹性网格模糊特征的手写体汉字识别方法

网格方向特征在手写体汉字识别系统中得到广泛应用,被认为是目前较成熟的手写体汉字特征之一。网格技术是网格方向特征的关键技术之一。根据汉字笔画分布特点及拓扑结构的相关性,提出了一种新的基于弹性网格及其相关模糊特征的提取方法。该方法使特征向量的信息量增加,特征更加稳定。对银行支票图像大写金额的识别率达到97.64%,实验结果证明本文方法比其他网格方向特征更有效。

隐马尔可夫模型在脱机手写体汉字识别中的应用

介绍了一种新的脱机手写汉字识别方法———隐马尔可夫模型 (HMM )法 ,该方法对每个汉字建立 8个HMM ,通过等比重综合方法将 8个分类器的计算结果进行综合 ,从而得到识别结果 ,实践证明该方法是可行的。

基于核聚类的SVM多类分类方法

对支持向量机的多类分类问题进行研究,提出了一种基于核聚类的多类分类方法。利用核聚类方法将原始样本特征映射到高维特征进行聚类分组,对每一组使用一个支持向量机二值分类器进行分类,并用这些二值分类器组成决策树的节点,构成了一个决策分类树。给出决策树的生成算法,提出了利用交叠系数来控制交叠,从而克服错分积累,提高分类准确率。实验结果表明,采用该方法,手写体汉字识别速度和正确率都达到了实用的要求。

小类别数手写汉字识别

针对小类别数手写汉字 ,在骨架图形的基础上 ,把手写汉字看作孤枝、孤环和部件的集合 ,并定义三者之间的方位关系 ,从而建立手写汉字的数学模型 .基于这种模型 ,进一步探讨一种新的识别方法以及新方法所使用的知识库的构造方法 .实验表明 。

基于笔划提取和合并的离线手写体汉字字符切分算法

手写体汉字字符切分是离线汉字字符识别预处理中的一个重要问题。针对离线手写体汉字 ,提出一种基于笔划提取和合并的手写体汉字字符切分算法。该算法首先基于方向游程提取汉字的笔划 ,并建立笔划框 ;再根据汉字笔划的结构知识对笔划框进行合并 ,得到字符的切分结果。该算法能较好地解决粘连汉字字符的切分问题 ,对从现场随机采集的 2 5 0 0封手写体信函地址汉字进行切分 ,单字正确率可达 91.5 %。