Image Binarization Based On ICA Approach for Optical Character Recognition

Image binarization plays a vital role in text segmentation which is used in OCR application. Binarization of text in degraded images is a challenging task due to the variations in colour, size, and font of the text and the results are often affected by complex backgrounds, different lighting conditions, shadows and reflections. A robust solution to this problem can significantly enhance the accuracy of scene text recognition algorithms leading to a variety of applications such as scene understanding, automatic localization, navigation, and image retrieval. In this paper, we propose a novel method to extract and binarize text from images that contains complex background. We use an Independent Component Analysis (ICA) based technique to map out the text region, which is inherently uniform in nature, while removing shadows, specularity and reflections, which are included in the background. This algorithm works better on images with different degradations. We implement our method on DIBCO dataset then we compare our robust algorithm with state-of-art criteria like binarization based on Otsu method and we can prove that our algorithm will give better results.