Sreeraj, M; Sumam, Mary Idicula(IEEE, December 7, 2012)
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Abstract:
The span of writer identification extends to broad
domes like digital rights administration, forensic expert decisionmaking
systems, and document analysis systems and so on. As the
success rate of a writer identification scheme is highly dependent
on the features extracted from the documents, the phase of
feature extraction and therefore selection is highly significant for
writer identification schemes. In this paper, the writer
identification in Malayalam language is sought for by utilizing
feature extraction technique such as Scale Invariant Features
Transform (SIFT).The schemes are tested on a test bed of 280
writers and performance evaluated
Sreeraj, M; Sumam, Mary Idicula(Association for Computer Science and Telecommunica, May , 2011)
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Abstract:
This paper presents a writer identification scheme for Malayalam documents. As the accomplishment rate of a scheme is highly dependent on the features extracted from the documents, the process of feature selection and extraction is highly relevant. The paper describes a set of novel features exclusively for Malayalam language. The features were studied in detail which resulted in a comparative study of all the features. The features are fused to form the feature vector or knowledge vector. This knowledge vector is then used in all the phases of the writer identification scheme. The scheme has been tested on a test bed of 280 writers of which 50 writers having only one page, 215 writers with at least 2 pages and 15 writers with at least 4 pages. To perform a comparative evaluation of the scheme the test is conducted using WD-LBP method also. A recognition rate of around 95% was obtained for the proposed approach
Sreeraj, M; Sumam, Mary Idicula(IEEE, December 9, 2009)
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Abstract:
This paper presents an efficient Online Handwritten
character Recognition System for Malayalam Characters
(OHR-M) using Kohonen network. It would help in
recognizing Malayalam text entered using pen-like devices. It
will be more natural and efficient way for users to enter text
using a pen than keyboard and mouse. To identify the
difference between similar characters in Malayalam a novel
feature extraction method has been adopted-a combination of
context bitmap and normalized (x, y) coordinates. The system
reported an accuracy of 88.75% which is writer independent
with a recognition time of 15-32 milliseconds
Description:
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009)