出版社：University of Malaya * Faculty of Computer Science and Information Technology
摘要：Script identification is a wellstudied problem for automatic processing of document images. Several attempts have been made so far, but it is still far ahead from the complete solution. In this paper, an automatic approach for linelevel handwritten script identification (HSI), considering eight official Indic scripts namely: Bangla, Devanagari, Kannada, Malayalam, Oriya, Roman, Telugu, and Urdu is proposed. We consider a 148dimensional feature vector using: image component fractal dimension, structural and visual appearance, directional stroke, interpolation and Gabor energy based texture features. For classification, we divide the whole script dataset based on different regions of India, to study a regionwise classification performance. Experimentation was carried out using the stateoftheart classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and fuzzy unordered rule induction algorithm (FURIA). Among all, we found that MLP as the best performer in terms of average accuracy of 98.2%, 99.5%, 99.1%, 99.5%, 99.9%, 98%, 98.9% for eightscript, biscript, eastern, north, south Indian script groups, scripts with ‘matra’ vs without ‘matra’, and dravidian vs. nondravidian groups respectively.
关键词：Handwritten script identification; image component fractal dimension; structural feature; directional stroke; interpolation based feature; gabor energy features; classification
