User Identification Using HMM and ANN - Patil, Vinita .B; Patil, Rajendra R (International Journal of Computer Sciences and Engineering, 2025)
The handwriting of a person is an important biometric attribute of a human being which can be used to authenticate human identity. A number of biometric techniques have been proposed for personal identification in the past. Handwriting by an authorized person is considered to be the “seal of approval” and remains the most preferred means of authentication. Handwritten recognition has been an active and challenging problem. Most of the traditional methods have two challenges, due to the large variations of written text and the dependency relationship between letters. First, in real applications, words may be written cursively, so it is hard to identify the words automatically. Even if the words are neat, different people may write the same words in different styles. Since there are large shape variations in human handwriting, recognition accuracy of handwritten words is very difficult. The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verification. A verification stage includes applying the extracted features of test handwriting to a neural network which will classify it as a genuine or forged. To recognize the handwritten words, the proposed work combines Artificial Neural Network (ANN) and Hidden Markov Model (HMM).