Identification of Scene Text by Character Descriptor in Smart Mobile Devices

Abstract— Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. The main focus of this system is on two character recognition methods. In text detection, previously proposed algorithms are used to search for regions of text strings. Proposed system uses character descriptor which is effective to extract representative and discriminative text features for both recognition schemes. The local features descriptor HOG is compatible with all above key point detectors. Our method of scene text recognition from detected text regions is compatible with the application of mobile devices. A personal digital assistant (PDA) was chosen because it combines small-size, computational resources and low cost price. Three key technologies are necessary: text detection, optical character recognition and speech synthesis. The demo system gives us details of algorithm design and performance improvements of scene text extraction. It is able to detect text region of text strings from cluttered and recognize characters in the text regions.