A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Geometry of Differential Operators with Application to Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVGIP: Graphical Models and Image Processing
Document Image Binarization Based on Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical approach to high resolution edge contour reconstruction
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Character Extraction from Noisy Background for an Automatic Reference System
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A noise attribute thresholding method for document image binarization
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
IBM Journal of Research and Development
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The sipping of ink through the pages of certain double-sided handwritten documents after long periods of storage poses a serious problem to human readers or OCR systems. This paper addresses this problem through the recovery of content on the front side of a page from the interfering image caused by the handwriting on the reverse side. First, by adapting the Gaussian stochastic model, the interfering model based on norm-orientation-discontinuity is proposed in analyzing the properties of the interfering strokes. Secondly, an improved canny edge detector with edge norm-orientation similarity constraint is applied. At the same time, two low thresholds are used to detect edges instead of a single low threshold. This improvement could link weaker foreground edges without introducing noises in the overlapping/overshadowed area. The proposed algorithms perform well regardless of the intensity differences between the image on the front side and the interfering image from the reverse side. The segmentation results of real images are shown and evaluated.