Script Identification from Printed Indian Document Images and Performance Evaluation Using Different Classifiers. (7th December 2014)

• Record Type: Journal Article
• Authors: Obaidullah, Sk Md; Mondal, Anamika; Das, Nibaran; Roy, Kaushik
• Other Names: Klement Erich Peter Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2014(2014)
• Publication Date: 2014-12-07
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2014/896128
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : Identification of script from document images is an active area of research under document image processing for a multilingual/ multiscript country like India. In this paper the real life problem of printed script identification from official Indian document images is considered and performances of different well-known classifiers are evaluated. Two important evaluating parameters, namely, AAR (average accuracy rate) and MBT (model building time), are computed for this performance analysis. Experiment was carried out on 459 printed document images with 5-fold cross-validation. Simple Logistic model shows highest AAR of 98.9% among all. BayesNet and Random Forest model have average accuracy rate of 96.7% and 98.2% correspondingly with lowest MBT of 0.09 s.