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A fuzzy features based online handwritten Bangla word recognition framework

机译:基于模糊特征的在线手写孟加拉语单词识别框架

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Handwriting recognition is one of the most important ways to ease the handling of information between man and machine. Online handwriting recognition can be a very attractive method when people feel inconvenient using keyboards to handle information with computing devices. The most complicated task associated with online Bangla handwritten recognition is to separate the adjacent characters and vowel signs from one another within a Bangla word. This problem becomes more complicated due to the variations of writing style of individuals. In this paper, we propose a framework to recognize handwritten Bangla words in real time considering different writing styles. We used fuzzy linguistic rules in order to recognize Bangla handwritten words. Evaluation result for various writing styles reveals that the propose framework can recognize Bangla handwritten words with 77% accuracy.
机译:手写识别是缓解人与机器之间信息的最重要方式之一。当人们觉得使用键盘处理与计算设备的信息不方便时,在线手写识别可能是一个非常有吸引力的方法。与在线Bangla手写识别相关的最复杂的任务是将相邻的字符和元音符号从Bangla字中的彼此分开。由于个人的写作风格的变化,这个问题变得更加复杂。在本文中,我们提出了一个框架,以便在考虑不同的写作风格的实时识别手写的孟加拉语言。我们使用模糊语言规则来识别孟加拉手写的话。各种写作风格的评估结果表明,提议框架可以识别孟加拉的手写单词,精度为77%。

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