Automatic cheque image processing is an important task in document image processing. Although many methods have been developed, it is still a challenge to find effective methods for some of the tasks, one of these is how to extract the filled-in strokes from a given cheque image. The problem becomes more difficult when a cheque has a complicated background. Most current methods are only applicable to simple binary images. This paper presents a method for processing complicated grey images in two steps. Both the extraction of reference lines and filled-in strokes are included. Our method is based on the construction of a pseudo 2D wavelet with adjustable rectangular supports. The experimental result shows good performance in the present method, which is also effective for slightly skewed cheque image.

Form document analysis is one of the most essential tasks in document analysis and recognition. One of the most fundamental and crucial tasks is the extraction of the reference lines which are contained in almost all form documents. This paper presents an efficient methodology for the complicated grey-level form image processing. We construct a non-orthogonal wavelet with adjustable rectangle supports and offer algorithms for the extraction of the reference lines based on the strip growth method using the multiresolution wavelet sub images. We have compared this system with the popular Hough transform (HT) based and the novel orthogonal wavelet based methods. As shown in the experiments, the proposed algorithm demonstrates high performance and fast speed for the complicated form images. This system is also effective for the form images with slight skew.

A new method that uses a modified fractal dimension theory to segment a document image and to discriminate between Oriental and European languages is presented in this paper. Two types of techniques have been usually adopted in language discrimination: token matching and statistical analysis. A modified fractal feature is used to discriminate the distinct textual structure complexities of Oriental and European languages. Experiments show that this method is effective and reliable for processing the document image even if it is skewed or contains noise that can not be removed clearly.

Based on wavelets, a theoretical method has been developed to process multi-gray level documents. In this method, two-dimensional multiresolution analysis, a wavelet decomposition algorithm, and compactly supported orthonormal wavelets are used to transform a document image into sub-images. According to these sub-images, the reference lines of a multi-gray level document can be extracted, and knowledge about the geometric structure of the document can be acquired. Particularly, this approach is more efficient to process form documents with gray level background. Experiments indicate that this new method can be applied to process documents with promising results.

One of the key technologies related to knowledge and data engineering is the acquisition of knowledge and data in the development and utilization of information system and the strategies to capture new knowledge and data. Actually, millions of documents, including technical reports, government files, newspapers, books, magazines, letters, bank checks, etc., have to be processed every day, and knowledge has to be acquired from them. This paper presents a new approach to document analysis for automatic knowledge acquisition. The traditional approaches have two major disadvantages: (1) They are not effective for processing documents with high geometrical complexity. Specially, the top-down approach can process only the simple documents which have specific format or contain some a priori information. (2) The top-down approach needs to split large components into small ones iteratively, while the bottom-up approach needs to merge small components into large ones iteratively. They are time consuming. This new approach is based on modified fractal signature. It can overcome the above weaknesses.

Based on wavelets, a new theoretical method has been developed to process form documents. In this method, two-dimensional multiresolution analysis (MSA), wavelet decomposition algorithm, and compactly supported orthonormal wavelets are used to transform a document image into sub-images. According to these sub-images, the reference lines of forms can be extracted, and knowledge about the geometric structure of the document can be acquired. Experiments prove that this new method can be applied to process documents with promising results.