RECOGNITION OF HISTORICAL RECORDS USING GABOR AND ZONAL FEATURES

The paper addresses the automation of the task of an epigraphist in reading and deciphering inscriptions. The automation steps include Pre-processing, Segmentation, Feature Extraction and Recognition. Preprocessing involves, enhancement of degraded ancient document images which is achieved through Spatial filtering methods, followed by binarization of the enhanced image. Segmentation is carried out using Drop Fall and Water Reservoir approaches, to obtain sampled characters. Next Gabor and Zonal features are extracted for the sampled characters, and stored as feature vectors for training. Artificial Neural Network (ANN) is trained with these feature vectors and later used for classification of new test characters. Finally the classified characters are mapped to characters of modern form. The system showed good results when tested on the nearly 150 samples of ancient Kannada epigraphs from Ashoka and Hoysala periods. An average Recognition accuracy of 80.2% for Ashoka period and 75.6% for Hoysala period is achieved.