Abstract
One of the most important topics in pattern recognition is text recognition. Especially offline handwritten recognition is a most challenging job due to the varying writing style of each individual. Here we propose offline Malayalam handwritten character recognition using probabilistic simplified fuzzy ARTMAP (PSFAM). PSFAM is a combination of SFAM and PNN (Probabilistic Neural Network). After preprocessing stage, scanned image is segmented into line images. Each line image is further fragmented into words and characters. For each character glyph, extract features namely cross feature, fuzzy depth, distance and Zernike moment features. Then this feature vector is given to SFAM for training. The presentation order of training patterns is determined using particle swarm optimization to get improved classification performance. The Bayes classifier in PNN assigns the test vector to the class with the highest probability. Best n probabilities and its class labels from PSFAM are given to SSLM (Statistical Sub-character Language Model) in the post processing stage to get better word accuracy.
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Vidya, V., Indhu, T.R., Bhadran, V.K., Ravindra Kumar, R. (2013). Malayalam Offline Handwritten Recognition Using Probabilistic Simplified Fuzzy ARTMAP. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_29
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DOI: https://doi.org/10.1007/978-3-642-32063-7_29
Publisher Name: Springer, Berlin, Heidelberg
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