Study of using hybrid deep neural networks in character extraction from images containing text

Abstract

Character segmentation from epigraphical images helps the optical character recognizer (OCR) in training and recognition of old regional scripts. The scripts or characters present in the images are illegible and may have complex and noisy background texture. In this paper, we present an automated way of segmenting and extracting characters on digitized inscriptions. To achieve this, machine learning models are employed to discern between correctly segmented characters and partially segmented ones. The proposed method first recursively crops the document by sliding a window across the image from top to bottom to extract the content within the window. This results in a number of small images for classification. The segments are classified into character and non-character class based on the features within them. The model was tested on a wide range of input images having irregular, inconsistently spaced, hand written and inscribed characters.