The Rectification of Document Images Using Text-features

Distortion representation is the key to the rectification of distorted document images. The text-lines are considered to be one of the most significant features of the images, which are extensively used by a majority of rectification algorithms. However, it is quite a challenge to accurately extract the text-lines of document images with distortions and other disruptive factors, such as non-textural objects. In this approach, we present a general document rectification method based on local distortion representation that is depicted by text-features instead of the text-lines. Specially, firstly, according to the similarity of local distortion, we divide the document image into local blocks. Secondly, a text-feature is exploited to depict the warping distortion of each block by considering the skew angle. Then, the rectification problem is formulated utilizing a reverse strategy according to the text-features. Finally, a perspective distortion is restored by making use of random sample consensus. The proposed method is appropriate for document images of multi-column layouts, multi-type fonts and non-textural objects. Various experiments have demonstrated the flexibility and high performance of the approach.