Foreground Text Extraction in Color Document Images for Enhanced Readability
Quite often it is observed that text information in documents is printed on colorful complex background. Smooth reading of text content in such documents is difficult due to background patterns and mix up of foreground text color with background color. Further the character recognition rate when such documents are OCRed, is low. In this paper we are presenting a novel approach for extraction of text information in complex color document images. The proposed approach is a three stage process. In the first stage the edge map is obtained utilizing the Canny edge operator. The edge map is split into blocks of uniform size and image blocks are classified as text or non-text. In each text block the possible text regions are identified and enclosed in tight bounding boxes using x-y cut on edge pixels. Further the text regions that are immediate adjacent to each other in vertical direction in which the character(s) are split horizontally are merged so as to enclose the character(s) fully in one text region. In the second stage certain amount of false text regions are eliminated based on a property of printed text. In the last stage the foreground text in each text region is extracted by unsupervised thresholding using the data of refined text regions. We conducted exhaustive experiments on documents having variety of background complexities with printed foreground text in any color, font and tilt. The experimental evaluations show that on an average 98.03% of text is identified. The processed document images showed better performance when OCRed compared with the corresponding unprocessed source document images.
KeywordsColor document image Complex background Foreground Text extraction Text region detection Unsupervised thresholding OCR
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