多字体印刷维吾尔文的切分

在许多文字识别系统中, 字符切分是预处理阶段的一部分, 其目的是从文本图象中分离出字母图象。而后才能针对切分后的每个字母进行识别。在具有连体特征的文字中, 字符切分就显得特别重要, 因为字符切分的准确与否直接影响字符的识别。维吾尔文就具有这种明显的连体特点, 本文主要讨论了采用抽取投影特征的方法, 实现了多字体维吾尔文的行切分、字切分和字符切分。     

A new methodology for gray-scale character segmentation andrecognition

Generally speaking, through the binarization of gray-scale images, useful information for the segmentation of touched or overlapped characters may be lost in many cases. If we analyze gray-scale images, however, specific topographic features and the variation of intensities can be observed in the character boundaries. In this paper, we propose a new methodology for character segmentation and recognition which makes the best use of the characteristics of gray-scale images. In the proposed methodology, the character segmentation regions are determined by using projection profiles and topographic features extracted from the gray-scale images. Then a nonlinear character segmentation path in each character segmentation region is found by using multi-stage graph search algorithm. Finally, in order to confirm the nonlinear character segmentation paths and recognition results, a recognition-based segmentation method is adopted. Through the experiments with various kinds of printed documents, it is convinced that the proposed methodology is very effective for the segmentation and recognition of touched and overlapped characters     

Recognition of Bangla compound characters using structural decomposition

In this paper we propose a novel character recognition method for Bangla compound characters. Accurate recognition of compound characters is a difficult problem due to their complex shapes. Our strategy is to decompose a compound character into skeletal segments. The compound character is then recognized by extracting the convex shape primitives and using a template matching scheme. The novelty of our approach lies in the formulation of appropriate rules of character decomposition for segmenting the character skeleton into stroke segments and then grouping them for extraction of meaningful shape components. Our technique is applicable to both printed and handwritten characters. The proposed method performs well for complex-shaped compound characters, which were confusing to the existing methods.     

脱机印刷体彝族文字识别系统的原理与实现

脱机印刷体彝文文字识别系统包括字符分割、特征提取、特征压缩以及字典匹配四个主要模块,该系统利用总结出的彝文字符合并和反合并规则提高了字符分割准确率,采用1024维周边方向贡献度作为彝文字符统计特征,对彝文中存在的大量相似字符具有良好的区分能力。系统还采用基于KL变换的特征压缩算法和三级字典快速匹配算法,最终实现了一个基于Windows平台的脱机印刷体彝文识别平台,该平台对样本的一次识别率在99.4%以上。实验结果表明这些方法是可行的和高效的。     

A deep learning based bank card detection and recognition method in complex scenes

With the widespread use of mobile Internet, mobile payment has become a part of daily life, and bank card recognition in natural scenes has become a hot topic. Although printed character recognition has achieved remarkable success in recent years, bank card recognition is not limited to traditional printed character recognition. There are two types of bank cards: unembossed bank cards, such as most debit cards which usually use printed characters, and embossed bank cards, such as most credit cards which mainly use raised characters. Recognition of raised characters is very challenging due to its own characteristics, and there is a lack of fast and good methods to handle it. To better recognize raised characters, we propose an effective method based on deep learning to detect and recognize bank cards in complex natural scenes. The method can accurately recognize the card number characters on embossed and unembossed bank cards. First, to break the limitation that YOLOv3 algorithm is usually used for object detection, we propose a novel approach that enables YOLOv3 to be used not only for bank card detection and classification, but also for character recognition. The CANNYLINES algorithm is used for rectification and the Scharr operator is introduced to locate the card number region. The proposed method can satisfy bank card detection, classification and character recognition in complex natural scenes, such as complex backgrounds, distorted card surfaces, uneven illumination, and characters with the same or similar color to the background. To further improve the recognition accuracy, a printed character recognition model based on ResNet-32 is proposed for the unembossed bank cards. According to the color and morphological characteristics of embossed bank cards, raised character recognition model combining traditional morphological methods and LeNet-5 convolutional neural network is proposed for the embossed bank cards. The experimental results on the collected bank card dataset and bank card number dataset show that our proposed method can effectively detect and identify different types of bank cards. The accuracy of the detection and classification of bank cards reaches 100%. The accuracy of the raised characters recognition on the embossed bank card is 99.31%, and the accuracy of the printed characters recognition on the unembossed bank card reaches 100%.

     

一种基于词片识别的字符分割算法

在字符识别领域,对粘连字符的识别是一个被广泛关注的技术难点,而且粘连字符的分割更是产生识别错误的主要原因之一.为了快速准确地进行字符分割,在总结已有方法的特点及不足的基础上,针对电子阅读笔系统的工作特点和实时性要求,提出并实现了一种面向电子阅读笔系统的基于词片识别的分割算法.该方法由于通过对字母组合的识别,降低了传统的基于孤立字符识别方法对于字符切分的要求,而且以中心生长法和改进的峰谷函数为切分工具来进行字符分割,简单实用,因而其在减少因粘连字符切分错误引起的识别错误的同时,不仅降低了运算复杂度,而且适合在阅读笔等嵌入式设备上应用.实验证明,该算法不仅效率高,而且实现简单,还能够降低分割错误带来的识别错误.     

文档识别中误切分字符拒识问题的研究

自动文档识别中字切分算法如果仅仅依靠大小位置等度量信息,很容易产生误切分图像块,需要字符分类器给出一定的反馈才能准确切分,为此提出了一个新的拒识算法,目标是尽可能准确地拒识非法字符。该文分析了基于距离的分类器的置信度和广义置信度,在此基础上改进了常用的广义置信度映射函数,并设计了一个基于样本学习的拒识规则,提高了拒识算法的适应性。在中日韩三种文档样本上的实验表明,该文算法明显改善了系统性能,对于较低质量的印刷文本识别具有一定的普遍意义。     

Analysis and recognition of highly degraded printed characters

This paper proposes an integrated system for the processing and analysis of highly degraded printed documents for the purpose of recognizing text characters. As a case study, ancient printed texts are considered. The system is comprised of various blocks operating sequentially. Starting with a single page of the document, the background noise is reduced by wavelet-based decomposition and filtering, the text lines are detected, extracted, and segmented by a simple and fast adaptive thresholding into blobs corresponding to characters, and the various blobs are analyzed by a feedforward multilayer neural network trained with a back-propagation algorithm. For each character, the probability associated with the recognition is then used as a discriminating parameter that determines the automatic activation of a feedback process, leading the system back to a block for refining segmentation. This block acts only on the small portions of the text where the recognition cannot be relied on and makes use of blind deconvolution and MRF-based segmentation techniques whose high complexity is greatly reduced when applied to a few subimages of small size. The experimental results highlight that the proposed system performs a very precise segmentation of the characters and then a highly effective recognition of even strongly degraded texts.     

印刷维吾尔文本切割

我国新疆地区使用的维吾尔文借用阿拉伯文字母书写。因为阿拉伯文字母自身书写的特点,造成维文文本的切割和识别极其困难。本文在连通体分类的基础上,结合水平投影和连通体分析的方法实现维文文本的文字行切分和单词切分。然后定位单词基线位置,计算单词轮廓和基线的距离,寻找所有可能的切点实现维文单词过切割,最后利用规则合并过切分字符。实验结果表明,字符切割准确率达到99 %以上。     

神经网络在车辆牌照字符识别中的应用

在车辆牌照自动识别系统中,因自然因素或采用因素使得原本原则的印刷字符产生畸变,给字符识别带来了很大困难。本文在特征抽取的基础上,采用ＢＰ网络进行分类,并附加线性感知器来实现单字的有效识别。该方法算法简便,识别率高,可适用于多种高噪声环境中的印刷字体识别。     

基于水平梯度和形态学分析的车牌实时定位方法

车牌图像定位是车牌照识别系统的关键，该文提出了一种在高速公路复杂背景下的车牌定位与车牌字符分割方法。该方法利用水平相关特征、车牌区域的梯度形态特征和车牌配色特征进行车牌定位，并利用车牌的结构特征采用多尺度模板匹配方法切分车牌字符。实验表明该方法在复杂背景下具有较好的定位切分效果和较强的鲁棒性。     

大规模逻辑神经网络印刷体汉字识别系统

逻辑神经网络是一种采用快速学习算法、ＲＡＭ阵列实现的数字网络。本文描述了采用这种网络模型实现的印刷体汉字识别系统。这是一个初步实用的系统，可识别大约４０００个不同字号的宋体汉字及其它字符，其识别率为９９％，对于实际书刊，识别率也能达到９５％。系统使用了大约３８４，０００个神经节点，是一个复杂的大规模神经网络。和其它同类系统相比，具有适应性、稳固性好，学习速度快以及可用数字集成电路全硬件并行实现等优     

Automated entry system for Chinese printed documents

In this paper, we present a new automated Chinese printed document entry system. This system features automated text/ graph segmentation, and multi-font, multi-size printed Chinese character recognition. Experimental results show that 95.8–99.4% of the top 10 printed characters can be correctly recognized, with the speed of 0.16 seconds/character.