ISSN(Online): 2320-9801 ISSN (Print) :2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 1, January 2015 Copyright to IJIRCCE 10.15680/ijircce.2015.0301004 19 Text Extraction from Natural Scene Images and Conversion to Audio in Smart Phone Applications M. Prabaharan 1 , K. Radha 2 M.E Student, Department of Computer Science and Engineering, Muthayammal Engineering College, India 1 Assistant Professor, Department of Computer Science and Engineering, Muthayammal Engineering College, India 2 ABSTRACT: Extracting text character from natural scene images is a challenging problem due to differences in text style, font, size, orientation, alignment and complex background. The text data present in images and video contain certain useful information for content-based information indexing and retrieval, sign translation and intelligent driving assistance. In scene text extraction, adjacent character grouping and character stroke orientation method is performed to search for image regions of text strings. Our proposed system is extracting a text and the extracted text information into audio. The Smart mobile phone application is used to show the effectiveness of our proposed method effectively. KEYWORDS: Scene Text Extraction, Character Stroke Orientation, Smart Phone Application I. INTRODUCTION Extracting text from images or videos is an important problem in many applications like document processing, image indexing, video content summary, video retrieval, video understanding. In natural scene images and videos, text characters and strings usually appear in nearby sign boards and hand-held objects and provide significant knowledge of surrounding environment and objects. Natural scene images usually suffer from low resolution and low quality, perspective distortion and complex background [1]. Scene text is hard to detect, extract and recognize since it can appear with any slant, tilt, in any lighting, upon any surface and may be partially occluded. Many approaches for text detection from natural scene images have been proposed recently. To extract text information by mobile devices from natural scene, automatic and efficient scene text detection and recognition algorithms are essential. The main contributions of this paper are associated with the proposed two recognition schemes. Firstly, a character descriptor is proposed to extract representative and discriminative features from character patches. It combines several feature detectors (Harris-Corner, Maximal Stable Extremal Regions (MSER), and dense sampling) and Histogram of Oriented Gradients (HOG) descriptors [5].Secondly, to generate a binary classifier for each character class in text retrieval; we propose a novel stroke configuration from character boundary and skeleton to model character structure. The proposed method combines scene text detection and scene text recognition algorithms. By the character recognizer, text understanding is able to provide surrounding text information for mobile applications, and by the character classifier of each character class, text retrieval is able to help search for expect objects from environment. Si milar to other methods, our proposed feature representation is based on the state of-the-art low-level feature descriptors and coding/pooling schemes. Different from other methods, our method combines the low-level feature descriptors with stroke configuration to model text character structure. Also, we present the respective concepts of text understanding and text retrieval and evaluate our proposed character feature representation based on the two schemes in our experiments. Besides, previous work rarely presents the mobile implementation of scene text extraction, and we transplant our method into an Android-based platform.