Stroke Based Handwritten Character Recognition

Abstract

Abstract. This work proposes a new stroke based methodology for handwritten character recognition. After the pre-processing, several steps are involved to achieve the recognition. First, the character is segmented into its strokes. Then, we determine the maximum length of the longest horizontal segment that can be inscribed on a stroke. We also compute that for the vertical direction. So we decide whether the stroke must be tagged as horizontal or vertical. After that, we represent by a string the vertical stroke position and its relational-ship with its adjacent horizontal strokes. In that string, each vertical stroke is represented by a character followed by a set of numbers which means where the adjacent horizontal strokes join the vertical one. A formal language grammar has been set and a knowledge base developed with known characters written by a single writer. Finally, an inference engine allow us to recognize unknown characters written by that single user. This algorithm has been tested on different writers and provides a hit rate of 87,13%.