Fuzzy stroke analysis of Devnagari handwritten characters
Prachi Mukherji, Priti P. Rege · 2008
Abstract:- Devnagari script is a major script of India widely used for various languages. In this work, we propose a fuzzy stroke-based technique for analyzing handwritten Devnagari characters. After preprocessing, the character is segmented in strokes using our thinning and segmentation algorithm. We propose Average Compressed Direction Codes (ACDC) for shape description of segmented strokes. The strokes are classified as left curve, right curve, horizontal stroke, vertical stroke and slanted lines etc. We assign fuzzy weight to the strokes according to their circularity to find similarity between over segmented strokes and model strokes. The character is divided into nine zones and the occurrences of strokes in each zone and combinations of zones are found to contribute to Zonal Stroke Frequency (ZSF) and Regional Stroke Frequency (RSF) respectively. The classification space is partitioned on the basis of number of strokes, Zonal Stroke Frequency and Regional Stroke Frequency. The knowledge of script grammar is applied to classify characters using features like ACDC based stroke shape, relative strength, circularity and relative area. Euclidean distance classifier is applied for unordered stroke matching. The system tolerates slant of about 10º left and right and a skew of 5º up and down. The system proves to be fast and efficient with regard to space and time and gives high discrimination between similar characters and gives a recognition accuracy of 92.8%.