Abstract
The aim of this study is to propose an efficient and fast framework for recognition of Kanji characters working in a real-time during their writing. Previous research on online recognition of handwritten characters used a large dataset containing samples of characters written by many writers. Our study presents a solution that achieves fine results, using a small dataset containing a single sample for each Kanji character from only one writer. The proposed system analyses and classifies the stroke types appearing in a Kanji and then recognises it. For this purpose, we utilise a Convolutional Neural Network and a hierarchical dictionary containing Kanji definitions. Moreover, we compare the histograms of Kanjis to solve the problem of distinguishing character having the same number of strokes of the same type, but arranged in a different position in relation to each other. The proposed framework was validated experimentally on online handwritten Kanjis by beginners and advanced learners. Achieved accuracy up to 89 % indicates that it may be a valuable solution for learning Kanji by beginners.
Highlights
K ANJI, along with the syllabic kana - hiragana and katakana, belong to the Japanese writing system
New studies should include this problem to develop algorithms that are less sensitive to strokes order. Having in mind these two problems, namely (i) the definition dictionary of Kanji characters and (ii) sensitivity of algorithms to strokes order, we propose a neural framework for online recognition of handwritten Kanji characters
We presented and implemented a complete framework that recognises Kanji characters in a real-time, based on successively drawn strokes
Summary
K ANJI, along with the syllabic kana - hiragana and katakana, belong to the Japanese writing system. They are adopted logographic Chinese characters and literally mean “Han characters” (漢 字). The number of Kanji characters is vast and amounts to over 500,000 [1]. Its main idea is that it analyses online Kanji characters when a student is writing them stroke by stroke. The analysis relies on checking the correctness of the drawn characters or suggesting their meaning. To make this possible, such a system has to recognize the handwritten Kanjis online
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