Hidden Markov Models (HMM) have been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for totally unconstrained Arabic handwritten word recognition based on a Model discriminant HMM is presented. A complete system able to classify Arabic-Handwritten words of one hundred different writers is proposed and discussed. The system first attempts to remove s...

Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks were trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighb...

The world heritage of handwritten Arabic documents is huge however only manual indexing and retrieval techniques of the content of these documents are available. To facilitate an automatic retrieval of such handwritten Arabic document, a number of automatic recognition systems for handwritten Arabic words have been proposed. Nevertheless, these systems suffer from low recognition accuracy due t...

handwritten digit recognition can be categorized as a classification problem. probabilistic neural network (pnn) is one of the most effective and useful classifiers, which works based on bayesian rule. in this paper, in order to recognize persian (farsi) handwritten digit recognition, a combination of intelligent clustering method and pnn has been utilized. hoda database, which includes 80000 p...

An alf&ithm for the recognition of unconstrained handwritten words is proposed. Based on an analysis of writing styles, it is shown that techniques for isolated character recognition. segmentation. as welJ as cursive script recognition are needed to achieve a robust solution to handwritten word recognition. A combination of these algorithms is proposed in which each method outputs a ranking of ...

We propose further improvement of a handwriting recognition method that avoids segmentation while able to recognize words that were never seen before in handwritten form. This method is based on the fact that few pairs of English words share exactly the same set of letter bigrams and even fewer share longer n-grams. The lexical n-gram matches between every word in a lexicon and a set of referen...

Offline Handwritten Word Recognition (HWR) plays a major role in the field of image processing and pattern recognition. Compared to online recognition, handwritten words cannot be identified easily because of the variations in the handwriting styles, type of paper used, quality of the scanner etc. In our paper we have focused on the Kannada handwritten word recognition. Large number of characte...

This thesis investigates ensemble methods for offline recognition of English handwritten text lines. Multiple recognisers are automatically generated from a single base recognition system. Combining the output of these multiple recognisers provides the final ensemble result. The underlying recognisers are based on hidden Markov models. One model is built for each character. Based on the lexicon...

This paper describes two approaches for Amharic word recognition in unconstrained handwritten text using HMMs. The first approach builds word models from concatenated features of constituent characters and in the second method HMMs of constituent characters are concatenated to form word model. In both cases, the features used for training and recognition are a set of primitive strokes and their...