Modeling and recognition of cursive words with hidden Markov models

In this paper, a new method for modeling and recognizing cursive words with hidden Markov models (HMM) is presented. In the proposed method, a sequence of thin fixed-width vertical frames are extracted from the image, capturing the local features of the handwriting. By quantizing the feature vectors of each frame, the input word image is represented as a Markov chain of discrete symbols. A handwritten word is regarded as a sequence of characters and optional ligatures. Hence, the ligatures are also explicitly modeled. With this view, an interconnection network of character and ligature HMMs is constructed to model words of indefinite length. This model can ideally describe any form of handwritten words, including discretely spaced words, pure cursive words and unconstrained words of mixed styles. Experiments have been conducted with a standard database to evaluate the performance of the overall scheme. The performance of various search strategies based on the forward and backward score has been compared. Experiments on the use of a preclassifier based on global features show that this approach may be useful for even large-vocabulary recognition tasks.     

Multifont size-resilient recognition system for Ethiopic script

This paper presents a novel framework for recognition of Ethiopic characters using structural and syntactic techniques. Graphically complex characters are represented by the spatial relationships of less complex primitives which form a unique set of patterns for each character. The spatial relationship is represented by a special tree structure which is also used to generate string patterns of primitives. Recognition is then achieved by matching the generated string pattern against each pattern in the alphabet knowledge-base built for this purpose. The recognition system tolerates variations on the parameters of characters like font type, size and style. Direction field tensor is used as a tool to extract structural features.     

On-line cursive script recognition: A user-adaptive system for word identification

We present the last version of our system Adresy dedicated to the recognition of words written on a digitizing tablet. Adresy is designed to be used with a big (given) vocabulary. In this context, it achieves a very good performance because it is able to learn automatically the writing style of any specific user, directly from a set of a few samples of words. Moreover, Adresy improves continuously its performance in a user-transparent way, thanks to a second, faster, learning process called adaptation. This paper describes the main aspects of Adresy. Moreover, the power of our system is proven through four experiments performed on a database of ten thousand handwritten words.     

Structural analysis of Arabic handwriting: segmentation and recognition

In this paper, a structural method of recognising Arabic handwritten characters is proposed. The major problem in cursive text recognition is the segmentation into characters or into representative strokes. When we segment the cursive portions of words, we take into account the contextual properties of the Arabic grammar and the junction segments connecting the characters to each other along the writing line. The problem of overlapping characters is resolved with a contour-following algorithm associated with the labelling of the detected contours. In the recognition phase, the characters are gathered into ten families of candidate characters with similar shapes. Then a heterarchical analysis follows that checks the pattern via goal-directed feedback control.     

An investigation of the modified direction feature for cursive character recognition

This paper describes and analyses the performance of a novel feature extraction technique for the recognition of segmented/cursive characters that may be used in the context of a segmentation-based handwritten word recognition system. The modified direction feature (MDF) extraction technique builds upon the direction feature (DF) technique proposed previously that extracts direction information from the structure of character contours. This principal was extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image.In order to improve on the DF extraction technique, a number of modifications were undertaken. With a view to describe the character contour more effectively, a re-design of the direction number determination technique was performed. Also, an additional global feature was introduced to improve the recognition accuracy for those characters that were most frequently confused with patterns of similar appearance. MDF was tested using a neural network-based classifier and compared to the DF and transition feature (TF) extraction techniques. MDF outperformed both DF and TF techniques using a benchmark dataset and compared favourably with the top results in the literature. A recognition accuracy of above 89% is reported on characters from the CEDAR dataset.     

Diacritical processing for unconstrained online handwriting recognition using a forward search

Out-of-order diacriticals introduce significant complexity to the design of an online handwriting recognizer, because they require some reordering of the time domain information. It is common in cursive writing to write the body of an `i' or `t' during the writing of the word, and then to return and dot or cross the letter once the word is complete. The difficulty arises because we have to look ahead, when scoring one of these letters, to find the mark occurring later in the writing stream that completes the letter. We should also remember that we have used this mark, so that we don't use it again for a different letter, and we should also penalize a word if there are some marks that look like diacriticals that are not used. One approach to this problem is to scan the writing some distance into the future to identify candidate diacriticals, remove them in a preprocessing step, and associate them with the matching letters earlier in the word. If done as a preliminary operation, this approach is error-prone: marks that are not diacriticals may be incorrectly identified and removed, and true diacriticals may be skipped. This paper describes a novel extension to a forward search algorithm that provides a natural mechanism for considering alternative treatments of potential diacriticals, to see whether it is better to treat a given mark as a diacritical or not, and directly compare the two outcomes by score. Received October 30, 1998 / Revised January 25, 1999     

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     

手写粉笔数字自动识别方法研究

手写粉笔字在生产生活中应用广泛,针对手写粉笔字易与背景混淆的特点,提出了手写粉笔数字自动识别方法,分别通过粉笔字普适性分割算法和基于SVM的手写数字分层分类自动识别技术解决了分割和识别两个关键问题,并结合钢材板坯号自动识别系统的应用进行方法的实验,达到了较高的识别率,具有重大的应用价值和推广意义。     

少数民族文字文本分析与识别的研究进展

对于少数民族古籍的保护与传承,国家予以高度重视,并强调了对这些不可再生文化资源透彻数字化的重要性。随着文档图像分析与识别技术的不断进步,对少数民族文字的文本分析与识别研究受到广泛关注,并取得显著成就,成为人工智能应用研究的一个热点领域。然而,由于少数民族文字种类繁多、应用场景多样及数据集的稀缺性等问题,这一研究领域仍面临诸多挑战。本文旨在总结先前的工作,并为未来的研究提供支持,重点讨论了印刷体文本、联机手写、古籍文档及场景文字识别等任务,概述了国内外在少数民族文种识别领域的发展和最新成果。首先阐明了少数民族文字文本分析与识别的重要性及其价值,介绍了特定少数民族文字及其古籍文档的特征。然后,回顾了这一领域的发展历史和现状,分析并总结了传统方法的代表性成果及其应用;详细讨论了研究重点向深度神经网络模型和深度学习方法的全面转移,这一转变使得各文种的识别性能得到了显著提升。最后,基于相关分析,本文指出了在不同文种文档分析与识别中存在的精度和泛化能力等方面的不足,以及与汉文文本分析与识别的差异;面对少数民族文字文本识别领域的主要困难与挑战,展望了未来的研究趋势和技术发展目标。     

Towards a web-based progressive handwriting recognition environment for mathematical problem solving

The emergence of pen-based mobile devices such as PDAs and tablet PCs provides a new way to input mathematical expressions to computer by using handwriting which is much more natural and efficient for entering mathematics. This paper proposes a web-based handwriting mathematics system, called WebMath, for supporting mathematical problem solving. The proposed WebMath system is based on client–server architecture. It comprises four major components: a standard web server, handwriting mathematical expression editor, computation engine and web browser with Ajax-based communicator. The handwriting mathematical expression editor adopts a progressive recognition approach for dynamic recognition of handwritten mathematical expressions. The computation engine supports mathematical functions such as algebraic simplification and factorization, and integration and differentiation. The web browser provides a user-friendly interface for accessing the system using advanced Ajax-based communication. In this paper, we describe the different components of the WebMath system and its performance analysis.     

建筑工程图的解释识别方法

建筑工程图的自动识别技术对于提高建筑业自动化、信息化程度十分重要。提出了一种解释识别方法，将识别过程需利用的对象层次结构、对象间关系、识别顺序等按照一定的格式加以描述，使得识别过程建立在对这种描述自动解释的基础上，较大地提高了自动识别系统的规范性、可维护性、可扩充性和稳定性。     

Extraction of spatial-temporal features for vision-based gesture recognition

One of the key problems in a vision-based gesture recognition system is the extraction of spatial-temporal features of gesturing.In this paper an approach of motion-based segmentation is proposed to realize this task.The direct method cooperated with the robust M-estimator to estimate the affine parameters of gesturing motion is used.and based on the dominant motion model the gesturing region is extracted,i.e.,the dominant object.So the spatial-temporal features of gestrues can be extracted.Finally,the dynamic time warping(DTW) method is directly used to perform matching of 12 control gestures(6 for “translation“ orders,6 for “rotation“orders).A small demonstration system has been set up to verify the method,in which a panorama image viewer can be controlled(set by mosaicing a sequence of standard“Garden“ images)with recognized gestures instead of the 3-D mouse tool.     

基于词缀的维吾尔谚语识别关键技术研究

在自然语言理解、机器翻译、舆情分析等自然语言处理领域中,维吾尔谚语识别是整个文本实体识别的重要组成部分。为满足维吾尔谚语信息化的需求,本文构建了比较完善的维吾尔谚语语料库。同时,从传统语言学角度对维吾尔谚语的语法、语义结构进行分析,构建了一个由维吾尔谚语功能语类（词缀）组成的、专属维吾尔谚语规则的知识库,并将此知识库与自然语言处理技术相结合,实现一个既能够从文本中识别出维吾尔谚语,又能提供维汉互译等功能的信息软件系统。该系统也为开展计算机理解与处理维吾尔文字奠定了一个崭新的基础。