A Nom historical document recognition system for digital archiving
A Nom historical document recognition system for digital archiving
复制标题
DOI：
10.1007/s10032-015-0257-8
复制
发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
T. V. Phan;K. Nguyen;M. Nakagawa
中科院分区：
文献类型：
--
作者：
T. V. Phan;K. Nguyen;M. Nakagawa
正在为数字存档开发一种使用图像二值化、字符分割和字符识别的匿名历史文档识别系统。它包括两个版本的脱机字符识别：一个用于自动识别扫描和分割的字符模式(7660个类别)，另一个用于用户手写输入(32,695个类别)。使用这种分离是因为在自动识别中包括较不频繁出现的类别增加了在没有关于姓名语言的可靠统计的情况下的误识率。此外，用户必须能够检查结果并从扩展的类别集中识别正确的类别，并且用户可以手动输入字符。这两个版本使用相同的识别方法，但它们使用不同的训练模式集进行训练。分割采用递归X-YCut图和Voronoi图，粗分类采用k-dtree和广义学习矢量量化，细分类采用改进的二次判别函数。该系统提供了一个界面，通过该界面，用户可以手动检查结果、更改二值化方法、校正分割和输入正确的字符类别。在提供了基本事实后，使用有限数量的NOM历史文件进行的评估表明，识别的两个阶段以及用户检查和更正显著改善了识别结果。
A Nom historical document recognition system is being developed for digital archiving that uses image binarization, character segmentation, and character recognition. It incorporates two versions of off-line character recognition: one for automatic recognition of scanned and segmented character patterns (7660 categories) and the other for user handwritten input (32,695 categories). This separation is used since including less frequently appearing categories in automatic recognition increases the misrecognition rate without reliable statistics on the Nom language. Moreover, a user must be able to check the results and identify the correct categories from an extended set of categories, and a user can input characters by hand. Both versions use the same recognition method, but they are trained using different sets of training patterns. RecursiveX–Ycut and Voronoi diagrams are used for segmentation;k–dtree and generalized learning vector quantization are used for coarse classification; and the modified quadratic discriminant function is used for fine classification. The system provides an interface through which a user can check the results, change binarization methods, rectify segmentation, and input correct character categories by hand. Evaluation done using a limited number of Nom historical documents after providing ground truths for them showed that the two stages of recognition along with user checking and correction improved the recognition results significantly.