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金融票据识别系统的应用研究
其他题名Applied Research of the Financial Document Analysis and Recognition System
殷绪成
学位类型工学博士
导师刘昌平
2006-06-06
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
关键词金融票据识别系统 票据分类 字符切分与识别 特征集成 文档图像压缩 Financial Document Analysis And Recognition System Form Classification Character Segmentation And Recognition Feature Combination Document Image Coding
摘要金融票据识别系统是当前文档分析与识别系统中的一个热点问题,包含票据分类、图像处理、字符切分与识别、以及文档图像压缩等一系列过程。本文对金融票据识别系统的多个方面进行了研究。在此基础上,本文建立了一个具有应用价值的金融票据识别系统,并已经应用于数家国内银行的上百套相关银行业务系统中。 本文的主要工作包括: 1. 针对种类多、数量大、版面复杂和噪声干扰严重的金融票据彩色图像,本文提出了一种基于二叉树决策的层次型票据类型匹配方法。该方法利用三个判断器:基于票据版面结构的松弛匹配、基于OCR的票据标题识别和基于票据颜色的色彩分析,层次化的进行票据类型判断。 2. 本文针对粘连断裂的印刷体数字行,提出了一种基于Viterbi算法的切分识别方案,该方案采用两次切分识别的层次型结构。在第二次切分识别过程中,首先,对于粘连断裂字符,在候选切分点区域,结合二值轮廓和灰度信息,采用基于Viterbi算法搜索的非直线路径进行切分,得到有效的切分路径;然后,结合分类器输出的可信度,采用Viterbi算法来合并前面得到的候选切分图像块,进行动态切分与识别。 3. 本文提出了一种基于Boosting变体算法的特征集成方法。在该Boosting运行的每一回中,训练得到多个不同的弱分类器,每一个分类器由一种类型的特征训练得到;然后,利用加权投票法结合这些弱分类器,形成一个新的中间分类器作为该回的分类器。最后,结合所得到的中间分类器,形成最终分类器。 4. 本文提出了一种利用JPEG2000,基于感兴趣区域技术(Region-Of-Interest,ROI)的文档图像编码方法。首先通过票据分类和目标检测,得到三种类型的ROI区域:信息提取区域、印章区域和手写体区域;然后,由这些ROI区域得到整张图像ROI的一个掩码层(Mask);最后,利用JPEG2000对票据图像进行压缩编码。 5. 基于实验室以前的研究和开发基础,结合前面几大问题的相关解决方案,我们实现了一个具有应用水平的金融票据识别系统,并且已经应用于银行具体的相关业务系统中。
其他摘要Recently, the Financial Document Analysis and Recognition System (FDARS) is a hot research topic, which includes form classification, image processing, character segmentation and recognition, document image coding, etc. In this dissertation, several key components of FDARS have been studied. Furthermore, an applied FDARS is implemented, which has been applied in more than one hundred bank-related systems. The research work in this dissertation can be described as follows: 1. In this dissertation, we introduce a hierarchical method for classifying financial documents using a binary tree decision, which includes three classifiers: the first classifier for elastic matching of the form structure, the second classifier for recognizing of the title of the document, and the third classifier for confirming of the color of the document. These classifiers hierarchically process a provided document image. 2. A segmentation and recognition system based on Viterbi algorithms is proposed for touching and broken printed numeral strings. This system includes two steps. In the second step, first, a segmentation method finds character nonlinear segmentation paths by combining gray scale and binary information based on a Viterbi algorithm; then, a recognition method uses a Viterbi algorithm to dynamically combine and recognize the character candidates with their reliabilities generated from the recognizer. 3. In this dissertation, a strategy of boosting based feature combination is introduced, where a variant of boosting is proposed to integrate different features. Different from the general boosting, at each round of this variant boosting, some weak classifiers are built on different feature sets, one of which is trained on one feature set. And then these classifiers are combined by weighted voting into a single one as the output classifier of this round. 4. An image compression algorithm with Region-Of-Interest (ROI) using JPEG2000 is proposed to code financial document images. Three types of ROIs: filled information ROIs, seal ROIs, and handwriting ROIs, are detected and extracted through document knowledge analysis and handwriting identification. A ROI mask with a random shape is constructed by thresholding and merging these ROIs. Finally, a financial document image is encoded using JPEG2000 Part 1 with this ROI mask. Compared to JPEG and DjVu, the method improves visual quality while decreasing storing space. 5. Based on the above techniques, we have designed and implemented a financial document analysis and recognition system, which has been applied in many financial-related systems of some Chinese banks.
馆藏号XWLW997
其他标识符200318014603040
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5940
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
殷绪成. 金融票据识别系统的应用研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2006.
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