Page segmentation is one of the important and basic research subjects of document analysis. There are two major kinds of page segmentation methods, i.e. hierarchical and no-hierarchical ones. Most traditional techniques such as top–down and bottom–up approaches belong to the hierarchical method. Though these two approaches have been used till now, they are not effective for processing documents with high geometric complexity and the process of splitting document needs iterative operations which is time consuming. A non-hierarchical method called the modified fractal signature (MFS) was presented in recent years. It can overcome the above weaknesses, however the MFS needs to calculate modified fractal signature which makes the theory very complex. In this thesis, we present a new page segmentation approach: Median Order Statistic Filter (MedOSF) — Maximum Order Statistic Filter (MaxOSF) approach which is more direct and much simpler. We use the MedOSF to remove the salt–pepper noise of the document and use the MaxOSF to do the page segmentation. In practice, they not only can adaptively process the documents with high geometrical complexity, but also save a lot of computing time.

This paper presents a novel approach to accelerate the 2D Mallat decomposition algorithm. In particular the proposed approach performs the 2D Mallat decomposition algorithm by way of computing the 2D cyclical convolution, and further completes the 2D cyclical convolution with the fast number theoretical transform (FNTT). Therefore, the 2D Mallat decomposition algorithm can be speeded up. The results obtained from the theoretical analysis and the experiment show that the 2D Mallat decomposition algorithm is significantly improved.

In this paper, a novel approach based on the wavelet orthonormal decomposition is presented to extract features in pattern recognition. The proposed approach first reduces the dimensionality of a two-dimensional pattern, and thereafter performs wavelet transform on the derived one-dimensional pattern to generate a set of wavelet transform subpatterns, namely, several uncorrelated functions. Based on these functions, new features are readily computed to represent the original two-dimensional pattern. As an application, experiments were conducted using a set of printed characters with varying orientations and fonts. The results obtained from these experiments have consistently shown that the proposed feature vectors can yield an excellent classification rate in pattern recognition.

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.

In this paper, we present a novel approach to optical character recognition that utilizes ring-projection-wavelet-fractal-signatures. In particular, the proposed approach reduces the dimensionality of a two-dimensional pattern by way of a ring-projection method, and thereafter, performs Daubechies' wavelet transform on the derived one-dimensional pattern to generate a set of wavelet sub-patterns, namely, curves that are non-self intersecting. Further from the resulting non-self intersecting curves, the divider dimensions are readily computed. These divider dimensions constitute a new characteristic vector for the original two-dimensional pattern, defined over the curves' fractal dimensions.

This paper presents a new approach to document analysis. The proposed approach is based on modified fractal signature. Instead of the time-consuming traditional approaches (top-down and bottom-up approaches) where iterative operations are necessary to break a document into blocks to extract its geometric (layout) structure, this new approach can divide a document into blocks in only one step. This approach can be used to process documents with high geometrical complexity. Experiments have been conducted to prove the proposed new approach for document processing.

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.