Optical Character Recognition on images with colorful background

In this paper, a preprocessing method is presented for improving Tesseract Optical Character Recognition (OCR) performance on images with colorful background. The proposed method consists of two steps. At first, a text segmentation method is performed which attempts to extract the text from the colorful background. This step is based on input image clustering into k images. In the second step, a classifier is used to identify the image containing text among k images resulting from the previous step. OCR is then performed on the identified image. The proposed preprocessing method improves Tesseract OCR performance by approximately 20%.