Discriminative Learning for Script Recognition
Document script recognition is one of the important preprocessing steps in a multilingual optical character recognition (MOCR) system. A MOCR system requires prior knowledge of script to accurately recognize multilingual text in a single document. In multilingual documents two scripts can be mixed together within a single text line. Many existing script recognition methods lack the ability to recognize multiple scripts mixed within a single text line. Besides, these methods usually use script dependent features for script recognition thereby limiting their scope to particularly that script. In this paper we propose a discriminative learning approach for multi-script recognition at connected component level by using a convolutional neural network. The convolutional neural network combines feature extraction and script recognition process in one step and discriminative features for script recognition are extracted and learned as convolutional kernels from raw input. This eliminates the need for manually defining discriminative features for particular scripts. Results show above 95% script recognition accuracy at connected component level on datasets of Greek-Latin, Arabic-Latin multi-script documents and Antiqua-Fraktur documents. The proposed method can be easily adapted to different scripts.