International Journal of Computer Vision
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
On the Evaluation of Document Analysis Components by Recall, Precision, and Accuracy
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Correcting Document Image Warping Based on Regression of Curved Text Lines
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Image Restoration of Arbitrarily Warped Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Restoring Warped Document Images through 3D Shape Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Dewarping using Robust Estimation of Curled Text Lines
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Segmentation Based Recovery of Arbitrarily Warped Document Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Pattern Recognition Letters
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Distortion always appears in document images while scanning thick bound volumes. There are two kinds of distortion for the scanned grayscale images, shadow appears at the volumes' spine area, and warping of the words occurs in the shadow. In this paper, a novel text boundary lines based method for efficient restoration of warped scanning Chinese document images is presented. We first detect on which side of an image the shadow lays by row grayscale analysis method. Then the shadow is removed by a modified Niblack's algorithm. In order to detect the warped feature, a text boundary lines' detection method is proposed. Finally, an adjustment method based on the text boundary lines is carried to restore the warped words. Experiments on 400 various scanning Chinese document images are implemented. The improvement on average character recall is 11.92% to 14.89%. Experiments show that the proposed restoration method is efficient for Chinese documents with both text and non-text regions.