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Segmentation of handwritten words into characters

机译：将手写单词分割为字符

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In this paper, SFF (Segmentation Facilitate Feature) technique is proposed to find the junction path to segment touched components based on the seed pixel selected among candidate pixels. Handwritten Recognition system has number of applications like reading postal address, filling forms, reading bank cheques, offering several challenges. In practice, constitute of the word images get touched in handwritten data due to variability in stroke, shortage of space which make the individual character extraction from the word image more complicated. Segmentation of individual in a word image requires a technique that takes care of the variability of writing. This paper proposed the SFF (Segmentation Facilitate Feature) technique to find seed pixel among candidate pixels based on 3-neighbouring pixels. It is used to find junction pixels which form a junction path to segregate the touched component. The junction path is selected to avoid the issues arising due to artifacts or deletion of components features. For experimentation, 1840 legal amount words containing touching components are used. The above number includes 250 words from benchmark database (ICDAR) and 1590 words are gathered from 15 different writers. On implementing, SFF (Segmentation Facilitate Feature) technique on the above mentioned database, 89.9% accuracy is achieved and a higher accuracy level 96.2% is achieved when performed on 1000 words containing two touching consonants.
机译：在本文中，提出了基于候选像素中选择的种子像素来找到对段触摸分量的结路径的SFF（分段促进特征）技术。手写识别系统具有阅读邮政地址，填充表格，阅读银行支票的应用程序数量，提供了多项挑战。在实践中，由于中风的可变性，使单独字符提取的空间短缺，从手写数据中触摸手写数据中的单词图像的构成。单词图像中的个体的分割需要一种处理写作变异性的技术。本文提出了基于3相邻像素的候选像素中寻找种子像素的SFF（分段促进特征）技术。它用于找到形成用于隔离触摸部件的结路径的结像素。选择结路径以避免由于伪像或删除组件特征而导致的问题。对于实验，使用1840个含有触摸组件的法律量单词。上面的数量包括来自基准数据库（ICDAR）的250个单词，从15个不同的作家收集1590个单词。在实施，在上述数据库上实现SFF（分段促进功能）技术，实现了89.9％的精度，并且在含有两个触摸辅音的1000字时执行更高的精度96.2％。

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