Fuzzy String Matching with a Deep Neural Network

A deep learning neural network for character-level text classification is described in this work. The system spots keywords in the text output of an optical character recognition system using memoization and by encoding the text into feature vectors related to letter frequency. Recognizing error messages in a set of generated images, dictionary and spell-check-based approaches achieved 69% to 88% accuracy, while various deep learning approaches achieved 91% to 96% accuracy, and a combination of deep learning with a dictionary achieved 97% accuracy. The contribution of this work to the state of the art is to describe a new approach for character-level deep neural network classification of noisy text.     

面向车牌字符分割的图像预处理方法研究

车牌字符分割是车牌识别系统中的核心步骤,而车牌预处理的效果直接关系到分割的准确率。针对传统基于灰度图的预处理方法难以消除由拍摄硬件和成像环境造成的干扰特征,提出一种基于R通道和灰度拉伸的车牌图像预处理方法。该算法将原始图像以R通道的数据表征,抑制车牌成像的干扰特征,提高了字符与背景底色的区分度;为了进一步增强图像的对比度,提出改进的灰度拉伸算法,有效分离字符和背景。为验证提出的预处理算法对字符分割的效果,引入一种基于投影和模板匹配的分割算法,实验表明,该算法不仅改善了污损车牌的成像效果,同时也有效提升了分割准确率。     

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中的难题之一。该文分析影响脱机手写体汉字识别性能的主要方面,如规范化方法、特征提取方法及分类方法,给出了每种方法的适用条件,介绍了目前研究中所使用的数据库。     

The state of the art in Japanese online handwriting recognition compared to techniques in western handwriting recognition

This paper compares the current state of the art in online Japanese character recognition with techniques in western handwriting recognition. It discusses important developments in preprocessing, classification, and postprocessing for Japanese character recognition in recent years and relates them to the developments in western handwriting recognition. Comparing eastern and western handwriting recognition techniques allows learning from very different approaches and understanding the underlying common foundations of handwriting recognition. This is very important when it comes to developing compact modules for integrated systems supporting many writing systems capable of recognizing multilanguage documents.Received: January 12, 2002, Accepted: March 6, 2003, Published online: 4 July 2003     

笔迹鉴别的字符予处理与匹配

笔迹鉴别多用匹配方法比较字并的书写风格, 而字符困像的预处理和归一化对匹配是昨常重要的本文介绍笔迹鉴别的字符图像预处理和一种形状匹配方法。预处理主要介绍二值图像的噪声消除和归一化方法。嗓声消除的方法是平滑、轮廓跟踪和填充为保持字符中的书写特征, 点阵的归一化是线性的, 但字符位五和尺度的确定昨常重要。本文给出了三种归一化方法四边定界法、重心对准法和单边定界法, 并在此基拙上用图像匹配方法进行书写人识别的实验。匹配方法是通过距离变换快速实现的。实验结果表明, 重心对·准归一化最适合于笔迹鉴别问题, 距离变换匹配得到的识别率也比较令人满意     

人民币冠字号码识别预处理算法研究

近年来,人民币冠字号码的识别受到越来越广泛的关注,其在打击经济犯罪,维持市场稳定和社会和谐等方面都具有很强的实用性和广阔的应用前景。一个稳定高效的人民币冠字码识别系统在很大程度上依赖于图像预处理的结果。提出了一套完整的人民币冠字码识别预处理方案,其中包括图像采集、倾斜校正、采集方向识别、冠字号码区域定位和二值化、字符提取等算法,并对三种冠字码区域二值化方法进行了比较和分析。实验结果表明,所提出的预处理方法精度很高,为后续的冠字码字符识别工作提供了可靠的技术保障。     

Off-line Arabic character recognition

A personal computer-based Arabic character recognition system that performs three preprocessing stages sequentially, thinning, stroke segmentation, and sampling, is described. The eight-direction code used for stroke representation and classification, the character classification done at primary and secondary levels, and the contextual postprocessor used for error detection and correction are described. Experimental results obtained using samples of handwritten and typewritten Arabic words are presented     

On-line computer recognition of proposed standard ANSI(USASI) handprinted characters

A robust real-time system for recognition of handprinted characters of the upper case English alphabet is described. The basic system is suited to implementation on small computers and has been designed to accept characters conforming to the stroke types and sequences suggested by a proposed ANSI(USASI) standard. Experiments with 2340 samples from 10 untrained subjects yielded an overall character recognition accuracy of 98.3%. The system is quite robust with respect to size and stylistic variations. The robustness and real-time operation of the system are largely attributed to the preprocessing and stroke identification techniques developed, which include a new two-stage syntactic classifier for the identification of curvilinear strokes.     

基于字符识别的实验报告自动打分系统的设计

针对实验教学中实验报告成绩的录入耗时耗力以及出错率高的问题,开发了基于字符识别［1］的实验报告自动打分系统,大幅提高了数据输入的工作效率和准确率。根据图像识别原理对成绩进行特征提取,采用BP神经网络系统,改进BP算法,识别出成绩,并利用Access数据库进行存储。系统经过测试,可以应用到实验报告中学号、成绩的快速录入与存储。     

Recognition of hand-printed Chinese characters using decision trees/machine learning C4.5 system

Recognition of Chinese characters has been an area of major interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character recognition: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This paper presents a new technique for the recognition of hand-printed Chinese characters using the C4.5 machine learning system. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The paper discusses Chinese character recognition using theHough transform for feature extraction and C4.5 system. The system was tested with 900 characters written by different writers from poor to acceptable quality (each character has 40 samples) and the rate of recognition obtained was 84%.     

An evaluation of statistical methods in handwritten hangul recognition

Although structural approaches have shown better performance than statistical ones in handwritten Hangul recognition (HHR), they have not been widely used in practical applications because of their vulnerability to image degradation and high computational complexity. Statistical approaches have not received high attention in HHR because their early trials were not promising enough. The past decade has seen significant improvements in statistical recognition in handwritten character recognition, including handwritten Chinese character recognition. Nevertheless, without a systematic evaluation on the effects of statistical methods in HHR, they cannot draw enough attention because of their discouraging experience. In this study, we comprehensively evaluate state-of-the-art statistical methods in HHR. Specifically, we implemented fifteen character normalization methods, five feature extraction methods, and four classification methods and evaluated their performances on two public handwritten Hangul databases. On the SERI database, statistical methods achieved the best performance of 93.71 % accuracy, which is higher than the best result achieved by structural recognizers. On the PE92 database, which has small number of samples per class, statistical methods gave slightly lower performance than the best structural recognizer.     

基于双边缘检测的车牌识别算法

随着智能交通的不断发展,车牌识别系统已经成为其中的重要组成部分。车牌识别分为车牌定位、字符分割以及字符识别三个部分。提出了一种新型车牌识别方法。在车牌定位方面,采用双边缘检测车牌定位方法;对于字符分割则提出了寻找连通域与传统投影分割相结合的方法;在字符识别上,将分类器分为三组,同时对于易混淆的字符进行了再次分类,这种做法缩短了训练时间,提高了准确率。实验结果表明,所提出的方法具有识别率高和速度快等特点。     

基于改进深度残差网络的矿井图像分类

精确煤矸分类及识别能力是煤矿智能煤矸分选机器人要解决的关键问题。在通过深度学习图像分类方法的检测煤矸石中,为克服当前残差网络计算量大、复杂度高以及信息丢失的问题,提出了基于改进深度残差网络的图像分类方法。并提出了一种新的损失函数soft-center loss,克服由于softmax分类器对特征的区分判别能力差以及易造成模型过度自信的问题。同时在图像预处理阶段利用CBDNet去噪网络,提高了井下图像的质量,进一步提升了煤矸分类的准确率。实验结果表明,基于改进深度残差网络分类模型相比于其他分类网络模型在井下图像分类准确率提高了4.12%,在公开数据集CIFAR-10准确率提高了1.5%。     

基于字符归一化双投影互相关性匹配识别算法

针对印刷体维吾尔文文字识别系统中的字符识别正确率较低这一难点问题,采用对字符图像进行横向扫描和纵向扫描生成行和列投影图, 结合三级分类,将目标字符与对应分类中的字符的双投影图逐一归一化并进行相关性均值计算的方法,取均值最大的字符作为最佳匹配识别结果,实现了对维文字符的识别。实验证明这种基于字符归一化双投影互相关性匹配识别算法方法抗干扰性强,简单易行,匹配精度高,使得印刷体维吾尔文字字符识别的正确率有了进一步提高。     

一种字符定位及识别技术在交通领域的应用

字符定位与识别技术在交通领域应用广泛.字符识别系统包括字符图像的拍摄、预先处理,将字符从图中截取出来,最后对字符对比甄别.采用灰阶处理、均值滤波等方法对图像预处理.在字符定位部分,采用边缘检测算法对字符图像在原始图中位置进行定位.在字符分割前,对字符进行二值化及倾斜校正.利用区域增长(region growing)方法...     

基于反馈的手写体字符识别方法的研究

该文提出了一种基于反馈的手写体字符识别方法。该方法将人工神经网络结构及学习算法运用于系统反馈机制中，并从理论上证明了该学习方法是收敛的，保证了算法的有效性。同时给出了反馈的可视化约束及反馈的判别准则。试验结果证明了该方法大大降低了高噪音手写体数字的识别率。该方法指出了一条进一步提高手写体字符系统性能的新途径。     

奇异值分解和稀疏自编码器的轴承故障诊断

针对滚动轴承故障特征提取和分类需要进行有监督训练才能实现等问题,提出了一种基于奇异值分解（SVD）和时域统计特征分析并结合堆栈稀疏自编码器（SAE）以及Softmax分类器实现滚动轴承故障诊断方法。该方法利用Hankle矩阵对原始数据进行矩阵重构,利用奇异值分解和时域分析对重构后的故障信号进行特征预提取,融合两种特征并输入到堆栈稀疏自编码器中进行特征优化,将优化后的特征输入到Softmax分类器中进行分类识别。实验结果表明,3种工况下10类故障数据的识别准确率均在96%左右,且高于文中其他方法,因此该方法能有效地进行滚动轴承复杂信号的特征预处理以及分类。     

Gujarati handwritten numeral optical character reorganization through neural network

This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work a neural network is proposed for Gujarati handwritten digits identification. A multi layered feed forward neural network is suggested for classification of digits. The features of Gujarati digits are abstracted by four different profiles of digits. Thinning and skew-correction are also done for preprocessing of handwritten numerals before their classification. This work has achieved approximately 82% of success rate for Gujarati handwritten digit identification.