Off-line Persian Handwritten Recognition Using Hidden Markov Models

Improving Off-line Handwritten Character Recognition with Hidden Markov Models

A method for the off-line recognition of handwritten characters with hidden Markov models (HMMs) is described. Performance of our HMM is compared to a baseline Naive Bayes classifier. Experimental results are given for different variants of our HMM algorithm, with optimizations for ignoring the effects of inter-word transitions when applying the Viterbi algorithm to return the most likely chara...

Off-line Arabic Handwritten Isolated Character Recognition using Hidden Markov Models

This paper presents a recognition system for Arabic handwritten isolated characters. The recognition system is based on hidden Markov model (HMM). The entire system is capable of recognizing the Arabic handwritten characters. First, the system removes all the variation in the character images. Second, Features are extracted using the sliding window technique with HMM. Then, the HMM is used for ...

Recognition of writer-independent off-line handwritten Arabic (Indian) numerals using hidden Markov models

This paper describes a technique for the recognition of optical off-line handwritten Arabic (Indian) numerals using hidden Markov models (HMM). The success of HMM in speech recognition encouraged researchers to apply it to text recognition. In this work we did not follow the general trend of using sliding windows in the direction of the writing line to generate features. Instead we generated fe...

Off-Line Sinhala Handwriting Recognition Using Hidden Markov Models

This paper describes a method to recognize off-line handwritten Sinhala characters, the language used by the majority of Sri Lanka. The classification approach is based on discrete hidden Markov models. A subset of the Sinhala alphabet was chosen for the study. The unknown characters are first pre-classified into one of three character groups, based on the structural properties of the text line...

Off-line cursive handwriting recognition using hidden markov models