An Efficient Text Recognition System from Complex Color Image for helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents. In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech. This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction algorithm based on the gamma correction method (GCM); and an optical character recognition (OCR) algorithm for text recognition. A detailed complexity study of the different blocks of this text recognition system has been realized. Following this study, an acceleration of the GCM algorithm (AGCM) is proposed. The AGCM algorithm has reduced the complexity in the text recognition system by 70% and kept the same quality of text recognition as that of the original method. To assist visually impaired persons, a graphical interface of the entire text recognition chain has been developed, allowing the capture of images from a camera, rapid and intuitive visualization of the recognized text from this image, and text-to-speech synthesis. Our text recognition system provides an improvement of 6.8% for the recognition rate and 7.6% for the F-measure relative to GCM and AGCM algorithms.