HMM and fuzzy logic: A hybrid approach for online Urdu script-based languages’ character recognition

Urdu script-based languages’ character recognition has some technical issues not existing in other languages and makes these languages more complicated. Segmentation-based character recognition approach for handwritten Urdu, both Nasta’liq and Nasakh script-based languages, incorporates number of overhead and very less accurate as compared to segmentation free. This paper presents a segmentation-free approach for recognition of online Urdu handwritten script using hybrid classifier, HMM and fuzzy logic. Trained data set consisting of HMMs for each stroke is further classified into 62 sub-patterns based on the primary stroke shape at the beginning and end using fuzzy rule. Fuzzy linguistic variables based on language structure are used to model features and provide suitable result for large variation in handwritten strokes. Twenty-six time variant structural and statistical features are extracted for the base strokes. The fuzzy classification into sub-patterns increases the efficiency and decreases the computational complexity due to reduction in data set size. The hybrid HMM–fuzzy technique is efficient for large and complex data set. It provided 87.6% and 74.1% for Nasta’liq and Nasakh, respectively, on 1800 ligatures.