Skew detection in document images based on rectangular active contour

Abstract

The digitalization processes of documents produce frequently images with small rotation angles. The skew angles in document images degrade the performance of optical character recognition (OCR) tools. Therefore, skew detection of document images plays an important role in automatic document analysis systems. In this paper, we propose a Rectangular Active Contour Model (RAC Model) for content region detection and skew angle calculation by imposing a rectangular shape constraint on the zero-level set in ChanVese Model (C-V Model) according to the rectangular feature of content regions in document images. Our algorithm differs from other skew detection methods in that it does not rely on local image features. Instead, it uses global image features and shape constraint to obtain a strong robustness in detecting skew angles of document images. We experimented on different types of document images. Comparing the results with other skew detection algorithms, our algorithm is more accurate in detecting the skews of the complex document images with different fonts, tables, illustrations, and layouts. We do not need to pre-process the original image, even if it is noisy, and at the same time the rectangular content region of a document image is also detected.

Automatic rectification of warped Bangla document images

“…In [27], the noise present in a document image is classified into six categories. They are ruled line noises [28], marginal noises [29], clutter noises [30], stroke‐like pattern noise [31], salt and pepper noises [32] and background noises [33]. Here we have used images from our WDID.…”

Applications of Text Detection and its Challenges

“…Tsai and Lee [74] proposed a technique for binarization of color documents using a decision tree that decides what features(saturation, luminance) to use for thresholding. Some of the most popular techniques for skew correction are using projection profiles [10], radon transforms [4], hough transform [7] and rectangular active contour model [25]. When documents are scanned from a book, distortions occur due the hard back cover.…”

A Language Independent Characterization of Document Image Noise in Historical Scripts

“…S. Banerjee et.al [9] Proposes a page edge detection algorithm that is a multiplicative combination of gradients and line based page edge detectors, a skew detection algorithm that is a linear combination of page/content edge and content based predictors, and a pipeline for skew correction and frame removal. H. Fan et.al [13] Proposed a Rectangular Active Contour Model (RAC Model) for content region detection and skew angle calculation by imposing a rectangular shape constraint on the zero-level set in the Chan-Vese Model (C-V Model) according to the rectangular feature of content regions in document images. Xiaoyi Jiang et.al [14] Describes an algorithm to estimate the skew angle of document images.…”

“…Image dilation and region labeling [1], Hough transform [2], Connected components [4], Inter slice crosscorrelation [8], Content based predictors [9], Rectangular active contour [13], Nearest neighbor clustering [14] Rotation [9] NA…”