3,881 research outputs found
An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition
Traditionally, the performance of ocr algorithms and systems is based on the
recognition of isolated characters. When a system classifies an individual
character, its output is typically a character label or a reject marker that
corresponds to an unrecognized character. By comparing output labels with the
correct labels, the number of correct recognition, substitution errors
misrecognized characters, and rejects unrecognized characters are determined.
Nowadays, although recognition of printed isolated characters is performed with
high accuracy, recognition of handwritten characters still remains an open
problem in the research arena. The ability to identify machine printed
characters in an automated or a semi automated manner has obvious applications
in numerous fields. Since creating an algorithm with a one hundred percent
correct recognition rate is quite probably impossible in our world of noise and
different font styles, it is important to design character recognition
algorithms with these failures in mind so that when mistakes are inevitably
made, they will at least be understandable and predictable to the person
working with theComment: 6pages, 5 figure
Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network
An off-line handwritten alphabetical character recognition system using
multilayer feed forward neural network is described in the paper. A new method,
called, diagonal based feature extraction is introduced for extracting the
features of the handwritten alphabets. Fifty data sets, each containing 26
alphabets written by various people, are used for training the neural network
and 570 different handwritten alphabetical characters are used for testing. The
proposed recognition system performs quite well yielding higher levels of
recognition accuracy compared to the systems employing the conventional
horizontal and vertical methods of feature extraction. This system will be
suitable for converting handwritten documents into structural text form and
recognizing handwritten names
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