English text localization and recognition from natural scene image

Now days reading words from an unconstrained and noisy image is not easy. Text localization and recognition in an image is a research area which takes efforts to develop a computer system with an ability to automatically read the text from images. The objective of this study is to propose a new method for text localization and recognition in natural scene images with complex background. In this paper, a hybrid methodology is suggested which extracts text from natural scene image with chaotic backgrounds. The proposed approach involves embedded system. This combines software with hardware. First, superimposed text regions in an image are extracted based on character descriptors features like Area, Bounding box, Perimeter, Euler number, Horizontal crossings. In the second step, superimposed text regions are tested for text content or non-text using character descriptors and SVM classifier. In the third step, detection of multiple lines in localized superimposed text regions is made and line segmentation is performed using horizontal profiles. In the final step, using vertical profiles each character of the segmented line is extracted. In the system ARM7 (LPC2138) is interface with Personal Computer. The GPS and GSM are also interface with the ARM7 (LPC2138). The extracted English text from an image is given to the ARM7 (LPC2138). This will be displayed on LCD. The GPS will obtain location coordinates of an image. The GSM will send SMS to the local tourist guide company like (e.g. Just Dial) to update information of natural scene image like (e.g. shop names, hotel names etc.). The workout has been done using images drawn from ICDAR 2013 and SVT 2010 datasets. The extracted text and location results will be played with IC (APR33A3). This system will be helpful for tourist and visually impaired. The results demonstrate the effectiveness of the proposed method, which can be used as an efficient method for text localization and recognition in natural scene images.