This paper addresses a problem of restoring handwritten archival documents by recovering their contents from the interfering handwriting on the reverse side caused by the seeping of ink. We present a novel method that works by first matching both sides of a document such that the interfering strokes are mapped with the corresponding strokes originating from the reverse side. This facilitates 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. The method has been shown to restore the documents effectively with average precision and recall rates for foreground text extraction at 84 percent and 96 percent, respectively.

The separation of overlapping text from graphics is a challenging problem in document image analysis. This paper proposes a specific method for detecting and extracting characters that are touching graphics. It is based on the observation that the constituent strokes of characters are usually short segments in comparison with those of graphics. It combines line continuation with the feature line width to decompose and reconstruct segments underlying the region of intersection. Experimental results showed that the proposed method improved the percentage of correctly detected text as well as the accuracy of character recognition significantly.

We present a novel method for processing double-sided historical handwritten documents using wavelets. The method is specially designed to remove interfering strokes from the reverse side due to ink seeping through pages after long periods of storage. The proposed method works by first matching both sides of a document page such that the interfering strokes are mapped with the originating strokes from the reverse side. Then with the rough identification of the foreground and the interfering strokes, a wavelet reconstruction process iteratively enhances the foreground strokes and smears the interfering strokes so as to strengthen the discriminating capability of an edge detector to extract a clear foreground image. This is an improvement of our earlier work.

Given the large number and complexity of Chinese characters, pattern matching based on structural decomposition and analysis is believed to be necessary and essential to off-line character recognition. The paper proposes a model of stroke extraction for Chinese characters. One problem for stroke extraction is how to extract primary strokes. Another major problem is to solve the segmentation ambiguities at intersection points. We use the degree information and the stroke continuation property to tackle these two problems. The proposed model can be used to extract strokes from both printed and handwritten character images.

This paper addresses a special problem with historical document images where handwritten characters from the reverse side appear as noise on the front side and even interfere with the front side characters. A novel method to extract clear textual images from interfering and overlapping areas of text is presented here. The proposed algorithm is interesting in that, with an observation that the edges of the seeping strokes from the reverse side are not as sharp as those on the front side, it adopts an edge detection approach to suppress unwanted background patterns. By further concentrating on the orientation of the strokes, other remaining long and strong noisy edges are removed by using an orientation filter and a size filter. The proposed method proves to perform well regardless of the intensity differences between the foreground writing and the interfering strokes. The segmentation results of real images are shown and evaluated.