基于词内部模式的新词识别

提出一种基于词内部模式的新词识别算法,该算法在重复串查找的基础上,结合词内部模式的特征提出改进位置成词概率和首尾单字成词概率的加权,依次判断互信息、邻接类别等统计量,对新词进行识别。通过不同的实验对比发现,该算法在一定程度上能有效提取新词。     

基于支持向量机的手写体相似字识别

本文提出对手写相似汉字进行识别的支持向量机方法。该方法与人工神经网络一样适用于小规模分类,但由于支持向量机依据结构风险最小化原则,因此泛化能力更强。并且,由于支持向量机算法是一个凸二次优化问题,能够保证找到的极值解就是全局最优解。本文用支持向量机算法对三组手写相似汉字进行了识别,取得了较好的结果。     

基于多维特征和候选项的易混手写英文识别

针对手写英文识别中易混字符的识别问题,提出一种结合多维特征和候选项以区分易混字符的识别方法.利用卷积神经网络(convolutional neural networks,CNN)对手写英文字符进行识别,根据初始字符识别信息确定易混字符的类别;利用多维特征,设计针对不同类别易混字符的识别规则;由易混字符和其相连字符组成候选项单词,结合语料库以及字符间构成关系,最终对易混字符进行识别判断.实验结果表明,该方法在解决了易混字符的识别问题后,识别手写英文字符的平均准确率达到98.67％,具有一定应用价值.     

基于笔划组合的维吾尔文联机手写单词连体段分割算法

从维吾尔文的特征和书写规则出发对维吾尔文联机手写单词识别技术进行了探索性研究,并提出一种新的思路:不是直接把单词切分成字母,而是先把单词分割成连体段,然后再分割成字母。这样,可以提高字母切分和字母识别的准确率。按照该思路,提出一种连体段分割算法:根据通过研究维吾尔文的特征和书写规则找出来的一些规则把一个个的笔画,组合成连体段。实验证明了该连体段分割思路和算法的可行性。     

基于MMW和ICER加权匹配的超声缺陷识别

提出了一种在不同缺陷类别判断时参数分配不同权值的加权匹配法,目的是在计算日标信号与某一具体类别缺陷信号之间的相似度时更具有针对性,从而避免冗余特征对缺陷识别带来不利影响.提出了一种ICER法确定加权算法中的权值,该方法考虑重构信号参数对缺陷识别的不同影响系数,并结合经验对权值进行分配.利用现场采集信号的测试结果表明,改进的Morlet小波能有效提高信号信噪比.采用ICER加权匹配法能有效提高缺陷类别识别的准确性.     

脱机自由手写英文单词的识别

介绍了一个基于隐马尔科夫模型的、采用模糊分割方式的脱机手写英文单词识别系统。该系统由图像预处理、特征提取、基于HMM的训练和识别四个模块组成。图像预处理中包括二值化、平滑去噪、倾斜校正和参考线提取。然后通过宽度不固定的滑动窗提取特征，前两组特征是整体形状和象素分布特征，另外又引入了Sobel梯度特征。HMM模型采用嵌入式的Baum-Welch算法训练，这种训练方式无需分割单词。最后用Viterbi算法识别。对字典中的每个单词，采用字母模型线性连接成单词模型。     

MHW蒙古文脱机手写数据库及其应用

建立公开、权威的蒙古文手写数据库是研究和开发蒙古文手写识别系统的基础。该文在蒙古文编码、构词和语法的研究基础上,公开了一个蒙古文大词汇量脱机手写数据库MHW,其中训练集由5 000个单词构成,每个词采集了20个样本,共包含10万样本,测试集Ⅰ包含5 000样本,测试集Ⅱ包含14 085样本。该文利用蒙古文文字长度可变特征研究了自动错误检测算法,提高了字库的可靠性。在三种常用手写识别模型上评估了字库的性能,其中基于循环神经网络的模型表现出最佳性能,在字典受限条件下测试集Ⅰ的词错误率达到2.20%,测试集Ⅱ达到了5.55%。     

脱机手写体满文文本识别系统的设计与实现

通过研究手写体圈点满文文字特征,提出采用基于笔画序列的脱机手写满文识别方法.首先使用数字图像处理方法对识别目标实现单词提取和预处理操作.然后将处理后的单词分解为笔画基元,采用统计模式识别方法进行识别,得到笔画序列.再把笔画序列转换为字根序列,使用模糊串匹配算法实现满文罗马转写的输出.最后再采用基于隐马尔可夫模型方法对单词识别结果进行后处理,进一步提高系统识别率.实验表明,在单一字体笔画学习和大语料双词同现概率统计的基础上,系统的识别率和自适应能力都较好.     

基于字素分割的蒙古文手写识别研究

隐马尔科夫模型(HMM)对序列数据有很强的建模能力,在语音和手写识别中都得到了广泛的应用。利用HMM研究蒙古文手写识别,首先需要解决的问题是手写文字的序列化。从蒙古文的构词和书写特点看,蒙古文由多个字素从上到下串联构成。选择字素集合和词的字素分割是手写识别的基础,也是影响识别效果的关键因素。该文根据蒙古文音节和编码知识确定了蒙古文字母集合,共包括1 171个字母。通过相关性处理、HMM排序筛选等手段得到长字素集合,共包括378个字素。对长字素经过人工分解,获得了50个短字素。最后利用两层映射给出了词转字素序列的算法。为了验证长短字素在手写识别中的效果,我们在HTK(hidden Markov model toolkit)环境下利用小规模字库实现了手写识别系统,实验结果表明短字素比长字素有更好的性能。文中给出的字素集合和词转字素序列的算法为后续基于HMM的蒙古文手写识别研究奠定了基础。     

基于多通道融合的连续手写识别纠错方法

在基于识别的界面中,用户的满意度不但由识别准确度决定,而且还受识别错误的纠正过程的影响.提出一种基于多通道融合的连续手写笔迹识别错误的纠正方法.该方法允许用户通过口述书写内容纠正手写识别中的字符提取和识别的错误.该纠错方法的核心是一种多通道融合算法.该算法通过利用语音输入约束最优手写识别结果的搜索,可纠正手写字符的切分错和识别错.实验评估结果表明,该融合算法能够有效纠正错误,计算效率高.与另外两种手写识别错误纠正方法相比,该方法具有更高的纠错效率.     

Recognizing handwritten Arabic words using grapheme segmentation and recurrent neural networks

The Arabic alphabet is used in around 27 languages, including Arabic, Persian, Kurdish, Urdu, and Jawi. Many researchers have developed systems for recognizing cursive handwritten Arabic words, using both holistic and segmentation-based approaches. This paper introduces a system that achieves high accuracy using efficient segmentation, feature extraction, and recurrent neural network (RNN). We describe a robust rule-based segmentation algorithm that uses special feature points identified in the word skeleton to segment the cursive words into graphemes. We show that careful selection from a wide range of features extracted during and after the segmentation stage produces a feature set that significantly reduces the label error. We demonstrate that using same RNN recognition engine, the segmentation approach with efficient feature extraction gives better results than a holistic approach that extracts features from raw pixels. We evaluated this segmentation approach against an improved version of the holistic system MDLSTM that won the ICDAR 2009 Arabic handwritten word recognition competition. On the IfN/ENIT database of handwritten Arabic words, the segmentation approach reduces the average label error by 18.5 %, the sequence error by 22.3 %, and the execution time by 31 %, relative to MDLSTM. This approach also has the best published accuracies on two IfN/ENIT test sets.     

Hidden Markov Model Based Word Recognition and Its Application to Legal Amount Reading on French Checks

A hidden Markov model (HMM) based word recognition algorithm for the recognition of legal amounts from French bank checks is presented. This algorithm is part of the A2iA INTERCHEQUE recognition system. The algorithm starts from images of handwritten words which have been automatically segmented from binary check images. After finding the lower-case zone on the complete amount, words are slant corrected and then segmented into graphemes. Then, features are extracted from the graphemes, and the feature vectors are vector quantized resulting in a sequence of symbols for each word. Likelihoods of all word classes are computed by a set of HMMs, which have been previously trained using either the Viterbi algorithm or the Baum–Welch algorithm. The various parameters of the system have been identified and their importance evaluated. Results have been obtained on large real-life data bases of French handwritten checks. The HMM-based system has been shown to outperform a holistic word recognizer and another HMM-type word recognizer from the A2iA INTERCHEQUE recognition system. Word recognition rates of about 89% for the 26-word vocabulary relevant for legal amount recognition on French bank checks have been obtained. More recently, a Neural Network–HMM hybrid has been designed, which produces even better recognition rates.     

印刷体朝鲜文字符中字母的分割与识别研究

朝鲜文是一种由元音和辅音构成的字母文字。因此经常使用的一种朝鲜文识别方法是:从朝鲜文字符中分离出每一个字母,然后对这些字母进行识别,最后确定识别字符。本文结合结构分析法,通过对字符图像背景进行细化处理,找到字母之间的分割线分离出了每个字母,并且利用两层外围距离特征对这些字母进行了识别。在对4种经常使用的朝鲜文印刷字体进行初步实验的结果表明,字母分割正确率平均达到了97.4% ,而字母样本集识别率为99%以上。     

Within-word pronunciation variation modeling for Arabic ASRs: a direct data-driven approach

Pronunciation variation is a major obstacle in improving the performance of Arabic automatic continuous speech recognition systems. This phenomenon alters the pronunciation spelling of words beyond their listed forms in the pronunciation dictionary, leading to a number of out of vocabulary word forms. This paper presents a direct data-driven approach to model within-word pronunciation variations, in which the pronunciation variants are distilled from the training speech corpus. The proposed method consists of performing phoneme recognition, followed by a sequence alignment between the observation phonemes generated by the phoneme recognizer and the reference phonemes obtained from the pronunciation dictionary. The unique collected variants are then added to dictionary as well as to the language model. We started with a Baseline Arabic speech recognition system based on Sphinx3 engine. The Baseline system is based on a 5.4 hours speech corpus of modern standard Arabic broadcast news, with a pronunciation dictionary of 14,234 canonical pronunciations. The Baseline system achieves a word error rate of 13.39%. Our results show that while the expanded dictionary alone did not add appreciable improvements, the word error rate is significantly reduced by 2.22% when the variants are represented within the language model.     

Region growing based segmentation algorithm for typewritten and handwritten text recognition

This paper presents a new technique of high accuracy to recognize both typewritten and handwritten English and Arabic texts without thinning. After segmenting the text into lines (horizontal segmentation) and the lines into words, it separates the word into its letters. Separating a text line (row) into words and a word into letters is performed by using the region growing technique (implicit segmentation) on the basis of three essential lines in a text row. This saves time as there is no need to skeletonize or to physically isolate letters from the tested word whilst the input data involves only the basic information—the scanned text. The baseline is detected, the word contour is defined and the word is implicitly segmented into its letters according to a novel algorithm described in the paper. The extracted letter with its dots is used as one unit in the system of recognition. It is resized into a 9 × 9 matrix following bilinear interpolation after applying a lowpass filter to reduce aliasing. Then the elements are scaled to the interval [0,1]. The resulting array is considered as the input to the designed neural network. For typewritten texts, three types of Arabic letter fonts are used—Arial, Arabic Transparent and Simplified Arabic. The results showed an average recognition success rate of 93% for Arabic typewriting. This segmentation approach has also found its application in handwritten text where words are classified with a relatively high recognition rate for both Arabic and English languages. The experiments were performed in MATLAB and have shown promising results that can be a good base for further analysis and considerations of Arabic and other cursive language text recognition as well as English handwritten texts. For English handwritten classification, a success rate of about 80% in average was achieved while for Arabic handwritten text, the algorithm performance was successful in about 90%. The recent results have shown increasing success for both Arabic and English texts.     

Neural Networks Pipeline for Offline Machine Printed Arabic OCR

In the context of Arabic optical characters recognition, Arabic poses more challenges because of its cursive nature. We purpose a system for recognizing a document containing Arabic text, using a pipeline of three neural networks. The first network model predicts the font size of an Arabic word, then the word is normalized to an 18pt font size that will be used to train the next two models. The second model is used to segment a word into characters. The problem of words segmentation in the Arabic language, as in many similar cursive languages, presents a challenge to the OCR systems. This paper presents a multichannel neural network to solve the offline segmentation of machine-printed Arabic documents. The segmented characters are then fed as an input to a convolutional neural network for Arabic characters recognition. The font size prediction model produced a test accuracy of 99.1%. The accuracy of the segmentation model using one font is 98.9%, while four-font model showed 95.5% accuracy. The whole pipeline showed an accuracy of 94.38% on Arabic Transparent font of size 18pt from APTI data set.