Handwritten Character Recognition using Brainnet Library

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dc.contributor.authorBabatunde, Akinbowale Nathaniel
dc.contributor.authorAbikoye, Oluwakemi Christiana
dc.contributor.authorBabatunde, Ronke Seyi
dc.contributor.authorKawu, R.O
dc.date.accessioned2018-02-21T13:45:25Z
dc.date.available2018-02-21T13:45:25Z
dc.date.issued2016
dc.description.abstractHandwriting 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 namesen_US
dc.identifier.citationBabatunde, 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); 129-136en_US
dc.identifier.urihttp://www.anale-informatica.tibiscus.ro/download/lucrari/14-2-18-Babatunde.pdf
dc.identifier.urihttp://hdl.handle.net/123456789/107
dc.language.isoenen_US
dc.publisherFaculty of Computers and Applied Computer Science, "Tibiscus" University of Timişoara, Romania.en_US
dc.subjectHandwritten Character Recognitionen_US
dc.subjectBrainNet Library.en_US
dc.subjectFeed Forward Neural Networksen_US
dc.subjectImage Processingen_US
dc.subjectFeature Extractionen_US
dc.titleHandwritten Character Recognition using Brainnet Libraryen_US
dc.typeArticleen_US

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