Comparative Analysis of Offline Handwriting Recognition Using Invariant Moments with HMM and Combined SVM-HMM Classifier

Offline Handwriting recognition is considered as important research field in the field of forensic and biometric applications. It finds significance in fields like graphology which exploits the physiological behavior of the person based on the handwriting. There are several algorithms for Handwriting recognition. However none of the techniques is yet proved to be satisfactory especially for large number of classes. This is due to the fact that handwriting is a pattern which differs from instance to instance of the same writer. Hence HMM is most preferred technique in this domain. It is due to the fact the HMM produces good result for large number of statistical patterns. However, the performance of the system depends entirely on the feature vectors. Unlike the cases of usual patter recognition like face recognition, a user's training and test sample may vary. Hence recognition of the same is tough. Therefore in this work we propose a novel technique for offline handwriting recognition based on Invariant Moments and curve let transform. Curvelet transform and Invariant moments are used predominantly for character recognition problem and hence are more suitable for the work. Further we compare the performance of HMM based technique with combined HMM-SVM based technique and found that for some combined HMM-SVM technique is better than HMM. Combined HMM-SVM classifier improve the problem of HMM classifier of multiple detection of Class too.