Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/4222
Title: DISCOVERING TAMIL WRITER IDENTITY USING GLOBAL AND LOCAL FEATURES OF OFFLINE HANDWRITTEN TEXT
Authors: Thendral, T
Vijaya, M S
Karpagavalli, S
Keywords: Classification
Feature Extraction
Support Vector Machine
Training
Writer Identification
Issue Date: 2013
Publisher: International Review on Computers and Software
Abstract: Writer identification is the process of identifying the individual based on their handwriting. Handwriting exhibits behavioral characteristics of an individual and has been considered as unique. The style and shape of the letters written vary slightly for same writer and entirely different for different writers. Also alphabets in the handwritten text may have loops, crossings, junctions, different directions etc. Hence accurate prediction of individual based on his/her handwriting is highly complex and challenging task. This paper proposes a new model for discovering the writer’s identity based on Tamil handwriting. Writer identification problem is formulated as classification task and a pattern classification technique namely Support Vector Machine has been employed to construct the model. It has been reported about 93.8% of prediction accuracy by RBF kernel based classification model.
URI: https://www.praiseworthyprize.org/jsm/index.php?journal=irecos&page=article&op=view&path%5B%5D=13672
Appears in Collections:2.Article (12)

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