A novel boundary growing approach for accurate skew estimation of binary document images
Skew angle estimation is an important component of optical character recognition (OCR) systems and document analysis systems (DAS). In this paper, a novel and an efficient method to estimate the skew angle of a scanned document image is proposed. The proposed method has two stages. In first stage, using boundary-growing approach, text lines containing characters of the scanned document image are extracted. From each text line, coordinates of the positions of the characters are obtained. In second stage, the obtained coordinates are fed to linear regression analysis (LRA) for the purpose of computation of skew angle. Several experiments have been conducted on various types of documents such as documents containing different language texts, documents with different fonts and documents with noise to reveal the robustness of the proposed method. A comparative study with the well-known methods is presented to show that the proposed method is superior in terms of accuracy and computational efficiency.
boundary growing optical character recognition document analysis linear regression analysis skew detection