Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading.

An overview of character recognition focused on off-line handwriting

Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550/spl times/3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise.

Document representation and its application to page decomposition

Automatic document processing: A survey

Document Image Understanding (DIU) is an interesting research area with a large variety of challenging applications. Researchers have worked from decades on this topic, as witnessed by the scientific literature. The main purpose of the present report is to describe the current status of DIU with particular attention to two subprocesses: document skew angle estimation and page decomposition. Several algorithms proposed in the literature are synthetically described. They are included in a novel classification scheme. Some methods proposed for the evaluation of page decomposition algorithms are described. Critical discussions are reported about the current status of the field and about the open problems. Some considerations about the logical layout analysis are also reported.

http://tev.fbk.eu/sites/tev.fbk.eu/files/doclayout_itc-irst-tr9703-09.pdf

Geometric Layout Analysis Techniques for Document Image Understanding: a Review

Extraction of text areas is a necessary first step for taking a complex document image for diameter recognition task. In digital libraries, such OCR'ed text facilitates access to the image of document page through keyword search. Gabor filters, known to be simulating certain characteristics of the human visual system (HVS), have been employed for this task by a large number of scientists, in scanned document images. Adapting such a scheme for camera based document images is a relatively new approach. Moreover, design of the appropriate filters to separate text areas, which are assumed to be rich in high frequency components, from nontext areas is a difficult task. The difficulty increases if the clutter is also rich in high frequency components. Other reported works, on separating text from nontext areas, have used geometrical/structural information like shape and size of the regions in binarized document images. In this work, we have used a combination of the above mentioned approaches for the purpose. We have used connected component analysis (CCA), in binarized images, to segment nontext areas based on the size information of the connected regions. A Gabor function based filter bank is used to separate the text and the nontext areas of comparable size. The technique is shown to work efficiently on different kinds of scanned document images, camera captured document images and sometimes on scenic images.

/pdf/gabor-filter-based-block-energy-analysis-for-text-extraction-3ksae6tbw9.pdf

Gabor filter based block energy analysis for text extraction from digital document images

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.

Dan Liu

Papers

A new approach to document analysis based on modified fractal signature