Text Detection from Natural Image using MSER and BOW

Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This project proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods. INTRODUCTION Recognition of text in natural scene images is becoming a prominent research area due to the widespread availability of imaging devices in low-cost consumer products like mobile phones. Detecting text in natural images, as opposed to scans of printed pages, faxes and business cards, is an important step for a number of Computer Vision applications, such as computerized aid for visually impaired and robotic navigation in urban environments. Retrieving texts in both indoor and outdoor environments provides contextual clues for a wide variety of vision tasks. The increasing market of cheap cameras, natural scene text has to be handled in an efficient way. Text in the image contains useful information which helps to acquire the overall idea behind the image. Character extraction from image is important in many applications. It is a difficult task due to variations in character fonts, sizes, styles and text directions, and presence of complex backgrounds and variable light conditions. Several methods for text (or character) extraction from natural scenes have been proposed. If we develop a method that extracts and recognizes those texts accurately in real time, then it can be applied to many important applications like document analysis, vehicle license plate extraction, textbased image indexing, etc and many applications have become realities in recent years Some works deal with text detection in the image while more recent ones point out the challenge of text extraction and recognition. Reading text from photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are (i) text detection from images and (ii) character recognition, and many recent methods have been proposed to design better feature representations and models for both.