Handwritten Word Recognition with Combined Classifier Based on Tri-grams
In the paper a new method of handwritten word recognition is described. In this method different probabilistic character language models (PCLMs) are used in order to improve the word recognition accuracy. These models consist of conditional probability distributions of characters given that preceding, succeeding or surrounding characters are known. The application of character succession and precedence in word recognition was explored in many earlier works. The novelty of the method proposed here consists in utilizing also the conditional probabilities based on character appearing of both sides of the letter being recognized and in combining such classifier with other ones based on character precedence and succession. This new classifier that uses two sided character neighborhood is realized as an iterative procedure which starts with character support factors evaluated independently for each character in the word by a soft character classifier. Then in each step of this iterative procedure the support factors are calculated as Bayes-like formula using needed probabilities. Next, the support factors obtained at the character level are used to calculate the support factors for complete words. Finally, the soft word recognition results obtained for classifiers based on three different PCLMs are combined. Experiments described in the paper show the superiority of the combined method over all its simpler components.
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