A comprehensive handwritten Indic script recognition system: a tree-based approach

A noteworthy achievement has been accomplished in developing optical character recognition (OCR) systems for different Indic scripts handwritten document images. But in a multi-script country like India, this cannot serve the entire purpose of document digitization when such multi-script document images need to be converted into machine readable form. But developing a script-invariant OCR engine is almost impossible. Therefore, in any multi-script environment, a complete framework of script identification module is very essential before starting the actual document digitization through OCR engine. Keeping this research need in mind, in this paper, we propose a novel handwritten script recognition model considering all the 12 officially recognized scripts in India. The classification task is performed at word-level using a tree-based approach where the Matra-based scripts are firstly separated from non-Matra scripts using distance-Hough transform (DHT) algorithm. Next, the Matra and non-Matra based scripts are individually identified using modified log-Gabor filter based features applied at multi-scale and multi-orientation. Encouraging outcomes establish the efficacy of the present tree-based approach to the classification of handwritten Indic scripts.