character recognition using neural networks thesis

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The developments of automatic systems that are capable of recognizing human handwritings offer a new way of improving human-computer interface and of enabling computers to perform repetitive tasks of reading and processing handwritten documents more efficiently. This approach creates the problem of junks - character hypotheses that are not true characters. Two training approaches have been introduced, namely character level discriminant training and word-level discriminant training.


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Different topologies of the ANN have been investigated for modeling of junks. Experimental results have shown that the ANN-HMM hybrid with word-level discriminant training consistently yield better recognition accuracy compared to character level discriminant training and discrete HMM-based recognition system.

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It achieves recognition accuracy of Repository Staff Only: item control page. Universiti Teknologi Malaysia Institutional Repository. Experimental results have shown that the ANN-HMM hybrid with word-level discriminant training consistently yield better recognition accuracy compared to character level discriminant training and discrete HMM-based recognition system.


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  8. It achieves recognition accuracy of Repository Staff Only: item control page. Universiti Teknologi Malaysia Institutional Repository.

    Abstract Cursive handwriting is the most natural way for humans to communicate and record information. More information and software credits. PDF 1MB. Marzuki b.

    Character Recognition using Neural Networks