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联机手写日文字符串识别
其他题名Online Handwritten Japanese Character String Recognition
于金伦
学位类型工学硕士
导师刘成林
2008-05-28
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词字符串识别 几何上下文 字符同步搜索 时间同步搜索 置信度转换 Character String Recognition Geometric Context Character-synchronous Search Frame-synchronous Search Confidence Transformation
摘要文字识别作为模式识别的一个重要应用领域,在过去的几十年中已经取得了非凡的成就。单个字符的识别率非常高,已经达到了实际应用的需要,并被广泛地应用于电脑汉字输入、手机、PDA等产品。随着时间的推移,单字识别已经无法满足人们的需要。整行文字、整段文字甚至整篇文本的识别已经成为人们新的需求。 整行文字的识别即字符串的识别是整篇文本识别的基础。虽然前人在字符串识别的研究领域做了大量的工作,但是时至今日,字符串的识别仍然没有达到实际应用的需要,还存在着识别精度不高、切分错误多、识别效率低等缺点和不足。字符串识别的主要难点是字符在被识别之前不能准确地切分,一般的解决方法是把字符切分和识别统一起来,通过组合搜索得到最优的切分和识别结果。本文主要针对字符串识别中的识别精度和搜索效率问题展开研究,并将有关方法用于日文手写字符串识别。 本文的工作主要包括以下三个方面: 一、本文将几何上下文信息与单字识别信息和语言上下文信息一起加入到字符串识别系统的路径评价准则,包括单字几何信息(一元几何信息)和字间几何信息(二元几何信息),提高了字符串的切分和识别精度,取得了很好的效果。 二、针对目前基于联合切分识别方法的字符串识别系统在搜索最优路径方面存在的搜索效率问题进行了研究,实现了字符同步搜索和时间同步搜索两种模式,并对这两种模式进行了深入分析和比较;同时提出一种改进的路径评价准则,使得动态规划算法可以应用于字符串识别过程中的最优路径搜索。 三、字符串识别系统中所用到的各种评价信息在度量尺度上不统一,本文采用置信度转换的方法,将分类器的输出(距离相似度量)转换成概率的形式,使参数调整更为方便。
其他摘要As a branch of pattern recognition, the character recognition field has seen tremendous progresses during the past decades. The recognition of single characters has reached sufficiently high accuracies, and has been applied to various areas like character input for personal computers, mobile phones, PDAs, and so on. In many cases, however, people writes characters continuously, and requires new technologies for recognizing sentences or even whole paragraphs. Character string recognition, i.e., the recognition of a text line or a sentence, is the essential problem of handwritten text recognition. Although many research works of character string recognition have been done in the past few years, the current performance of character segmentation and recognition is still far from satisfaction for practical applications. The major difficulty of character string recognition is that the characters are hard to be segmented before they are recognized. A general solution is to integrate character segmentation and recognition in a combinatorial optimization framework. This dissertation proposes some strategies to improve the accuracy and search efficiency of character string recognition, and applies the methods to online handwritten Japanese character string recognition. The main contributions of this dissertation are as follows: First, we propose a machine learning based approach for modeling geometric context, to be used together with character recognition scores and linguistic context for evaluating search paths in integrated segmentation and recognition of handwritten character strings. By modeling both unitary geometry information (single-character geometry) and binary geometry information (character-pair geometry), the character segmentation and recognition accuracies were improved significantly. Second, this dissertation aims to improve the search efficiency of optimal segmentation-recognition path in character string recognition system. We implement and compare two search modes: character-synchronous search and frame-synchronous search, under normalized and un-normalized path scoring criteria. Also, we propose a modified path criterion which satisfies the principle of optimality such that the DP (dynamic programming) algorithm can be used to search for the optimal path. Third, due to the discrepancy of measurement ranges of different scores (character recognition, geometry, language) evaluating the candidate paths, it is difficult to tune the weighting parameters in the path criterion. We use confidence transformation methods to convert the (similarity/distance) scores into probabilities, such that the tuning of weighting parameters becomes easier.
馆藏号XWLW1193
其他标识符200528014628055
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7435
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
于金伦. 联机手写日文字符串识别[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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