Off-Line Handwritten Character Recognition with Hidden Markov Models

Handwritten Character recognition is a process, which associates a symbolic meaning with letters, symbols and numbers drawn on an image. Many researches have been done to solve handwritten character recognition in the areas such as Image Processing, Pattern Recognition, and Artificial Intelligence etc. Recognition of offline handwritten character is a goal of much research effort in pattern recognition. Many techniques have been applied for recognition of handwritten characters but still it is the case of less efficiency and accuracy of recognition. Thus this paper brings out a complete system to recognize offline handwritten characters using Hidden Markov Model (HMM), in which an artificial neural network is trained to identify similarities and patterns among different handwritten samples with high accuracy. HMM has a Freedom to manipulate the training and verification processes. HMMs are very powerful modeling tools than many statistical methods.