  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.
