An introduction to digital image processing
An introduction to digital image processing
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVGIP: Graphical Models and Image Processing
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Multiscale Segmentation of Unstructured Document Pages Using Soft Decision Integration
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Wavelet Approach to Extracting Contours of Document Images
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
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 National Archives of Singapore keeps a large number of double-sided handwritten archival documents. Over long periods of storage, ink sipped through the pages of these documents, resulting in interfering images of handwriting coming from the back of the page. This paper addresses this problem of segmenting handwriting from both sides of a document by means of a wavelet approach. We first match both sides of a document page such that the interfering strokes are mapped with the corresponding strokes originating from the reverse side. This allows the identification of the foreground and interfering strokes. A wavelet reconstruction process then iteratively enhances the foreground strokes and smears the interfering strokes so as to strengthen the discriminating capability of an improved Canny edge detector against the interfering strokes. Experimental results confirm the validity of the wavelet approach.