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Cognitive Analysis for Reading and Writing of Bengali Conjuncts

机译:孟加拉语合词读写的认知分析

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In this paper, we study the difficulties arising in reading and writing of Bengali conjunct characters by human-beings. Such difficulties appear when the human cognitive system faces certain obstructions in effortlessly reading/writing. In our computer-based investigation, we consider the reading/writing difficulty analysis task as a machine learning problem supervised by human perception. To this end, we employ two distinct models: (a) an auto-derived feature-based Inception network and (b) a hand-crafted feature-based SVM (Support Vector Machine). Two commonly used Bengali printed fonts and three contemporary handwritten databases are used for collecting subjective opinion scores from human readers/writers. On this corpus, which contains the perceptive ground-truth opinion of reading/writing complications, we have undertaken to conduct the experiments. The experimental results obtained on various types of conjunct characters are promising.
机译:在本文中,我们研究了人类在阅读和书写孟加拉语合词时遇到的困难。当人类认知系统在不费力地阅读/书写时遇到某些障碍时,就会出现此类困难。在基于计算机的调查中,我们将阅读/写作难度分析任务视为由人类感知监督的机器学习问题。为此,我们采用了两个不同的模型:(a)一个基于特征的自动衍生的Inception网络,以及(b)一个基于特征的手工制作的SVM(支持向量机)。两种常用的孟加拉语印刷字体和三种现代手写数据库被用来收集人类读者/作家的主观意见分数。在这个包含阅读/写作复杂性的可察觉事实真相的语料库中,我们已着手进行实验。在各种类型的连字符上获得的实验结果是有希望的。

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