Scene to Text Conversion and Pronunciation for Visually Impaired People

The recent technological advancements are focusing on developing smart systems to improve the quality of life. Machine learning algorithms and artificial intelligence are becoming elementary tools, which are used in the establishment of modern smart systems across the globe. In this context, an effective approach is suggested for automated text detection and recognition for the natural scenes. The incoming image is firstly enhanced by employing Contrast Limited Adaptive Histogram Equalization (CLAHE). Afterward, the text regions of the enhanced image are detected by employing the Maximally Stable External Regions (MSER) feature detector. The non-text MSERs are removed by employing appropriate filters. The remaining MSERs are grouped into words. The text recognition is performed by employing an Optical Character Recognition (OCR) function. The extracted text is pronounced by using a suitable speech synthesizer. The proposed system prototype is realized. The system functionality is verified with the help of an experimental setup. Results prove the concept and working principle of the devised system. It shows the potential of employing the suggested method for the development of modern devices for visually impaired people.