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In this paper, we present a segmentation-based method for offline Farsi handwritten word recognition. Although most segmentation-based systems suffer from segmentation errors within the first stages of recognition, using the inherent features of the Farsi writing script, we have segmented the words into sub-words. Instead of using a single complex classifier with many (N) output classes, we have created N simple recurrent neural network classifiers, each having only true/false outputs with the ability to recognize sub-words. Through the extraction of the number of sub-words in each word, and labeling the position of each sub-word (beginning/middle/ end), many of the sub-word classifiers can be pruned, and a few remaining sub-word classifiers can be evaluated during the sub-word recognition stage. The candidate subwords are then joined together and the closest word from the lexicon is chosen. The proposed method was evaluated using the Iranshahr database, which consists of 17,000 samples of Iranian handwritten city names. The results show the high recognition accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Online handwriting recognition, OHR, has gained a widespread use in everyday life. In some scripts such as Farsi and Arabic, additional strokes are written after the main stroke. These delayed strokes include dots and small signs. In this paper, the delayed strokes effect was studied from two points of views: subword modeling and lexicon reduction. The model of a subword was made of concatenating the main body model and the delayed strokes models. Hidden Markov model, HMM, was employed as a classifier. The delayed strokes of an input subword were additionally exploited to reduce the lexicon size. Our proposed method was tested on TMU-OFS dataset, including 1000 online Farsi subwords, and a recognition rate of 85.2% was achieved. [Copyright &y& Elsevier]

Abstract: Online handwriting is formed by a combination of horizontal and vertical trajectories. If these trajectories are treated separately, new recognition methods are emerged. In contrast, one classifier is often used to recognize handwriting. In this work, some features for x(t) and y(t) signals were proposed and used to make two separate classifiers. After initial recognition by these classifiers, their results were fused for final recognition. Using HMM classifiers and simple product rule for decision fusion, the recognition results of 42 classes of Farsi subwords showed promising achievements. [Copyright &y& Elsevier]