Off-line Handwritten Script Identification from Eastern Indian Document Images Using Logistic Model Tree

摘要

Script identification from document images is a complex real-life problem for a multi-script country like India where 13 official scripts are present. To develop an optical character recognizer for a specific language, it is necessary to identify the script first by which the document is written. In this paper, scripts from the off-line handwritten document images written by any one of the four popular scripts in eastern India, namely Bangla, Roman, Devanagari, and Oriya, are identified. A document-level approach is followed for the same. Using some mathematical, structural, and script-dependent feature, a multi-dimensional feature set is constructed. Finally, logistic model tree (LMT) is applied for classification and an average accuracy rate of 95.5 % is obtained with a fivefold crossvalidation.