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

A robust and fast skew detection algorithm for generic documents

Recent advances in intelligent character recognition are enabling us to address many challenging problems in document image analysis. One of them is intelligent form analysis. This paper describes a generic system for form dropout when the filled-in characters or symbols are either touching or crossing the form frames. We propose a method to separate these characters from form frames whose locations are unknown. Since some of the character strokes are either touching or crossing the form frames, we need to address the following three issues: 1) localization of form frames; 2) separation of characters and form frames; and 3) reconstruction of broken strokes introduced during separation. The form frame is automatically located by finding long straight lines based on the block adjacency graph. Form frame separation and character reconstruction are implemented by means of this graph. The proposed system includes form structure learning and form dropout. First, a form structure-based template is automatically generated from a blank form which includes form frames, preprinted data areas and skew angle. With this form template, our system can then extract both handwritten and machine-typed filled-in data. Experimental results on three different types of forms show the performance of our system. Further, the proposed method is robust to noise and skew that is introduced during scanning.

A generic system for form dropout

Hough transform based fast skew detection and accurate skew correction methods

A symbol-connected diagram is mainly composed of symbols and their connections. All symbols can be categorized into two groups: shape symbols and structure symbols. This paper proposes a system for automatic understanding of symbol-connected diagrams based on a graph, called the consistent attributed graph (CAG), which represents the diagram in a bottom-up fashion. The shape symbol processing is carried out by means of shape processing, followed by shape symbol identification with the use of two symbol windows in the CAG. Some experimental results for understanding logic circuit diagrams are given.

Automatic understanding of symbol-connected diagrams

An isothetic polygon, which has all its edges oriented in two orthogonal directions, is one of the simplest representations for a contour of binary images. This paper introduces an efficient, onepass algorithm for generating the isothetic polygons for the contours in binary images.

Isothetic polygon representation for contours

An algorithm is described which produces the extraction of an object's contours in a binary image while storing only two raster lines at a time during processing. The major data structure used is a block adjacency graph which occupies much less space (typically one hundredth) than the original image. Because this algorithm has very low costs of memory space and processing time, it is a powerful tool for processing large-size engineering drawings. Applications of the method to the recognition of some loop-structure logical symbols are described. >

Xinggang Lin

Papers

Isothetic polygon representation for contours

An economical contour extraction algorithm for understanding large-size engineering drawings

The tree representation of the graph used in binary image processing