549 research outputs found
Neural Networks for Handwritten English Alphabet Recognition
This paper demonstrates the use of neural networks for developing a system
that can recognize hand-written English alphabets. In this system, each English
alphabet is represented by binary values that are used as input to a simple
feature extraction system, whose output is fed to our neural network system.Comment: 5 pages, 3 Figure, ISSN:0975 - 888
Recognition of Cursive Arabic Handwritten Text using Embedded Training based on HMMs
In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models HMMs The system is analytical without explicit segmentation used embedded training to perform and enhance the character models Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image These features are modelled using hidden Markov models and trained by embedded training The experiments on images of the benchmark IFN ENIT database show that the proposed system improves recognitio
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