Text Recognition in Mobile Images using Perspective Correction and Text Segmentation

It is significant that adopt text recognition at mobile devices to care human’s health. We observed that although OCR is very suit for recognizing scanned documents, it has poor performation on mobile photoes, which suffer from unequal lighting, clutter, skew, or poor image quality. Therefore, a new algorithm is proposed that take a series of measures to deal with these tough situations of mobile images. This work includes three main steps. Firstly we adopt perspective correction to rectify the distortion of an image. Secondly we use filter to further eliminate the effect of noisy in image. Finally we apply text segmentation to effective measure each text row of image. Compared to OCR text recogniztion success rate 34.7%, the success rate of our method is 65.8%. Experimental results show that the proposed algorithm greatly improves the accuracy of text recognition.