The task of online handwriting recognition (HR) becomes often challenging due to the presence of confusing characters which are separable by a small region. To address this problem, we propose a “discriminative region (DR) selection” technique which highlights the discriminative region that distinguishes one character from another similar character. The existing DR selection approach for online handwriting often finds spurious DR when the intra-class shape variations become higher than the distinction between DRs of the two characters. The proposed technique which is an improved version of the existing approach can minimize the effect of high intra-class variations and results in robust DR selection. In addition, we propose an online HR system enabling DR-based processing in a single-stage classification framework. The use of hidden Markov model and support vector machine classifiers is explored to develop the HR system. The efficacy of the proposals is shown for character and word recognition tasks and evaluated on three databases: the locally collected Assamese character database, UNIPEN English character database and UNIPEN ICROW-03 word database. The recognition results are promising over the reported works.

中文翻译：

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用于在线手写识别的改进的区分区域选择方法

由于存在混乱的字符，这些字符可被小区域分开，因此在线手写识别（HR）的任务通常变得具有挑战性。为了解决这个问题，我们提出了一种“区分区域（DR）选择”技术，该技术着重强调了将一个字符与另一个相似字符区分开的区分区域。当类内部形状变化变得高于两个字符的DR之间的区别时，现有的用于在线手写的DR选择方法通常会发现伪造的DR 。所提出的技术是对现有方法的改进版本，可以最大程度地降低高类别内部的影响变化并导致可靠的灾难恢复选择。此外，我们提出了一个在线人力资源系统，可在单阶段分类框架中实现基于DR的处理。探索了使用隐马尔可夫模型和支持向量机分类器来开发人力资源系统。显示了该提案对字符和单词识别任务的功效，并在三个数据库上进行了评估：本地收集的阿萨姆语字符数据库，UNIPEN英文字符数据库和UNIPEN ICROW-03单词数据库。识别结果对已报道的作品是有希望的。