Handwritten Character Recognition using Brainnet Library
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Computers and Applied Computer Science, "Tibiscus" University of Timişoara, Romania.
Abstract
Handwriting has continued to persist as a
means of communication and recording information in dayto-
day life even with the introduction of new technologies.
Given its ubiquity in human transactions, machine
recognition of handwriting has practical significance, as in
reading handwritten notes in a PDA, in postal addresses on
envelopes, in amounts in bank checks, in handwritten fields
in forms, etc. An off-line handwritten alphabetical
character recognition system using multilayer feed
forward neural network is described, and a method, called,
diagonal based feature extraction is used for extracting the
features of the handwritten alphabets. This project
implements this methodology using BrainNet Library. Ten
data sets, each containing 26 alphabets written by
various people, are used for training the neural
network and 130 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, if modified
will be suitable for converting handwritten documents into
structural text form and recognizing handwritten names
Description
Keywords
Handwritten Character Recognition, BrainNet Library., Feed Forward Neural Networks, Image Processing, Feature Extraction
Citation
Babatunde, A.N., Abikoye, O.C., Babatunde, R.S., & Kawu, R.O. (2016): Handwritten Character Recognition using Brainnet Library. Anale. Seria Informatică( Annals. Computer Science Series ). 14(2); 129-136