Denoising and Segmentation of Epigraphical Scripts

This paper is a presentation of a new method for denoising images using Haralick features and further segmenting the characters using artificial neural networks. The image is divided into kernels, each of which is converted to a GLCM (Gray Level Co-Occurrence Matrix) on which a Haralick Feature generation function is called, the result of which is an array with fourteen elements corresponding to fourteen features The Haralick values and the corresponding noise/text classification form a dictionary, which is then used to de-noise the image through kernel comparison. Segmentation is the process of extracting characters from a document and can be used when letters are separated by white space, which is an explicit boundary marker. Segmentation is the first step in many Natural Language Processing problems. This paper explores the process of segmentation using Neural Networks. While there have been numerous methods to segment characters of a document, this paper is only concerned with the accuracy of doing so using neural networks. It is imperative that the characters be segmented correctly, for failing to do so will lead to incorrect recognition by Natural language processing tools. Artificial Neural Networks was used to attain accuracy of upto 89%. This method is suitable for languages where the characters are delimited by white space. However, this method will fail to provide acceptable results when the language heavily uses connected letters. An example would be the Devanagari script, which is predominantly used in northern India.