Script independent online handwriting recognition

The most general form of handwriting style is mixed cursive and this is the most difficult type in view of its automatic recognition. Similar handwriting style are prevalent in various scripts such as English, Arabic, Bengali etc. Handwriting recognition for such a script gets further difficult whenever its alphabet consists of a large number of characters like Bengali which has around 350 characters. Hidden Markov models (HMM) are the most popularly used architectures for similar recognition problems. However, the task becomes easy if the underlying lexicon depending upon the specific application is provided. In such situations, holistic or word-based recognition approach is adopted which does not require recognition of the constituent characters. On the other hand, the same task gets complicated as the lexicon size increases and / or it consists of many similar shape words. In a recent study [1] of similar situation, a fully connected non-homogeneous HMM has been used where its observation sequence was generated through explicit segmentation of the input word. In the present study, we have explored that the performance of this HMM-based recognition scheme is independent of both the script and the particular intelligent segmentation strategy. We implemented a novel segmentation scheme based on Discrete Curve Evolution algorithm [2] and two other existing segmentation methods on standard databases of English, Arabic and Bangla to arrive at the above conclusion. Statistical hypothesis testings of the simulation results further confirm the above claim.