Grantha script recognition from ancient palm leaves using histogram of orientation shape context

Abstract

Grantha script, which was used for writing sacred texts in Sanskrit language. Grantha script contains valuable information, but these historical document images suffer from noises present in the original document due to its degradation, faint ink strokes, unwanted impurities, background images, bleed through, aging of the palm leaves and so on. It includes handwritten characters and also it is an extinct language. The incentive behind this research work include presenting a novel recognition system for modern Grantha script characters and also confirming the link between Malayalam and Grantha script. After pre-processing the input image, universe of discourse is selected. Feature extraction plays a vital role in the proposed recognition process. The proposed method uses HOOSC (Histogram of Orientation Shape Context) feature extraction, which is new in character recognition, but used in some other area and ANN (Artificial Neural Network) for classification. Feature extraction methods which are used for other languages and that can be used in Grantha script like HOG (Histogram of Oriented Gradients), Gabor features, Zoning, and Invariant Moments provides classification accuracy of 84%, 76.3%, 76%, and 52% respectively. The recognized characters are mapped to corresponding Malayalam characters, and proposed method provides an accuracy of about 96.5%.