An Autonomous Learning System of Bengali Characters Using Web-Based Intelligent Handwriting Recognition

Nazma Khatun, Jouji Miwa

Abstract


This research project was aimed to develop an intelligent Bengali handwriting education system to improve the literacy level in Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. Here, we developed a prototype of web-based (iPhone/smartphone or computer browser) intelligent handwriting education system for autonomous learning of Bengali characters that allows students to do practice their handwriting at anywhere at any time. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide colourful error feedback to the students to correct themselves. Bengali is a multi-stroke input characters with extremely long cursive shape where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older people. Finally, we conducted a survey in Bangladesh for the performance analysis of our proposed education system. The experimental results showed that our autonomous learning methodology helped to improve the average recognition accuracy by 4.1% (from 87.2% to 91.4%) with average Mean-Opinion-Score 4.1. It confirmed that the successful use of web-based Bengali handwriting education system can be very helpful to improve the literacy level in Bangladesh within a very short period.


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DOI: http://dx.doi.org/10.5539/jel.v5n3p122

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