Malayalam Offline Handwritten Recognition Using Probabilistic Simplified Fuzzy ARTMAP

Autor:	V. Vidya, T. R. Indhu, R. Ravindra Kumar, V K Bhadran
Rok vydání:	2013
Předmět:	business.industry Computer science Feature vector Probabilistic logic Pattern recognition Bayes classifier Fuzzy logic Probabilistic neural network ComputingMethodologies_PATTERNRECOGNITION Pattern recognition (psychology) Feature (machine learning) Artificial intelligence Language model business
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783642320620 ISI
DOI:	10.1007/978-3-642-32063-7_29
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f359a289cd5aaabf418a3e3c4691f6d7 https://doi.org/10.1007/978-3-642-32063-7_29 Zobrazit plný text záznamu