The contributions to document image analysis of 99 papers published in the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) are clustered, summarized, interpolated, interpreted, and evaluated.

Twenty years of document image analysis in PAMI

Document Image Analysis

This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.

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Robust and accurate vectorization of line drawings

The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.

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Symbol Recognition: Current Advances and Perspectives

Accurate and efficient vectorization of line drawings is essential for their higher level processing. We present a thinningless sparse pixel vectorization (SPV) algorithm. Rather than visiting all the points along the wire's black area, SPV sparsely visits selected medial axis points. The result is a crude polyline, which is refined through polygonal approximation by removing redundant points. Due to the sparseness of pixel examination and the use of a specialized data structure, SPV is both time efficient and accurate, as evaluated by our proposed performance evaluation criteria.

Sparse pixel vectorization: an algorithm and its performance evaluation

A system for interpretation of images of paper-based line drawings is described. Since a typical drawing contains both text strings and graphics, an algorithm has been developed to locate and separate text strings of various font sizes, styles, and orientations. This is accomplished by applying the Hough transform to the centroids of connected components in the image. The graphics in the segmented image are processed to represent thin entities by their core-lines and thick objects by their boundaries. The core-lines and boundaries are segmented into straight line segments and curved lines. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Such a description includes the location and attributes of simple polygonal shapes, circles, and interconnecting lines, and a description of the spatial relationships and occlusions among them. Hatching and filling patterns are also identified. The performance of the system is evaluated using several test images, and the results are presented. The superiority of these algorithms in generating meaningful interpretations of graphics, compared to conventional data compression schemes, is clear from these results. >

A system for interpretation of line drawings

A system for automatic recognition and description of graphical primitives and their interconnections in a mixed text/graphics image is described. The input to the system is a high-resolution binary image obtained by scanning paper-based documents. The Hough transform is applied to the connected components of the image to locate and separate text strings of various font size and orientation. The graphics in the segmented image is processed to represent thin entities by their core lines and thick objects by their boundaries. Lines of various types are identified and segmented into straight line and curved line segments during this process. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Various hatching and filling patterns in the image are identified and described. The algorithm has been implemented as software, and its robustness and performance have evaluated tested using a number of test images. The results for a simple test image are reported. >

Sing-tze Bow

Papers

A system for interpretation of line drawings

A system for recognition and description of graphics