Abstract
The main problem of Bangla (Bengali) and Devanagari handwriting recognition is the shape similarity of characters. There are only a few pieces of work on writer-independent cursive online Indian text recognition, and the shape similarity problem needs more attention from the researchers. To handle the shape similarity problem of cursive characters of Bangla and Devanagari scripts, in this article, we propose a new category of features called ‘ sub-stroke-wise relative feature ’ (SRF) which are based on relative information of the constituent parts of the handwritten strokes. Relative information among some of the parts within a character can be a distinctive feature as it scales up small dissimilarities and enhances discrimination among similar-looking shapes. Also, contextual anticipatory phenomena are automatically modeled by this type of feature, as it takes into account the influence of previous and forthcoming strokes. We have tested popular state-of-the-art feature sets as well as proposed SRF using various (up to 20,000-word) lexicons and noticed that SRF significantly outperforms the state-of-the-art feature sets for online Bangla and Devanagari cursive word recognition.
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