Automated systems for understanding display boards are finding many applications useful in guiding tourists, assisting visually challenged and also in providing location aware information. Such systems require an automated method to detect and extract text prior to further image analysis. In this paper, a new approach that uses zonewise profile features to identify and segment text regions from low resolution images of display boards captured from mobile phone cameras is presented. The method computes zonewise profile features on every 40 × 40 pixel image block and identifies potential text blocks using newly defined discriminant functions. Further, a merging algorithm is used to merge text blocks to obtain text regions. The method is implemented using the android software development kit and experimented on Sony X-PeriaTM Z C6603/C6602 mobile.The proposed methodology is evaluated on 3240 low resolution images of display boards captured from 2 and/or 5 mega pixel cameras on mobile phones at various pixel sizes 240 × 320, 480 × 640 and 960 × 1280 and reports an average processing time of 13 s and detection rate of 95.5%. The proposed method is found to be robust and insensitive to the variations in size and style of font, thickness and spacing between characters. . His research areas include image processing, pattern recognition, character recognition, soft computing, optimization and graph theoretic techniques, internet of things and web technology. He is a co-author of a book on C-programming language. He is a life member of professional bodies like IETE and ISTE, and member of IEEE. His research areas include: Image processing and pattern recognition, text extraction, text recognition, language understanding and machine translation, fuzzy systems, and neural networks. He is member of professional bodies like IEEE and ISTE. 1550017-20 Int. J. Image Grap. 2015.15. Downloaded from www.worldscientific.com by UNIVERSITY OF PITTSBURGH on 08/13/15. For personal use only.