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1

Direct Tensor Voting in line segmentation of handwritten documents

Babczyński Tomasz, Ptak Roman

International Journal of Electronics and Telecommunications

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2024

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Vol. 70, No. 1

95--102

EN

In the vast archives and libraries of the world, countless historical documents are tucked away, often difficult to access. Thankfully, the digitization process has made it easier to view these invaluable records. However, simply digitizing them is not enough – the real challenge lies in making them searchable and computer-readable. Many of these documents were handwritten, which means they need to undergo handwriting recognition. The first step in this process is to divide the document into lines. This article introduces a solution to this problem using tensor voting. The algorithm starts by conducting voting on the binary image itself. Then, using the local maxima found in the resulting tensor field, the lines of text are precisely tracked and labeled. To ensure its effectiveness, the algorithm’s performance was tested on the data-set delivered by the organizers of the ICDAR 2009 competition and evaluated using the criteria from this contest.

2

Land Map Image Dataset: Ground-Truth And Classification Using Visual And Textural Features

Mandal S., Biswas S., Kumar-Das A., Chanda B.

Image Processing & Communications

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2014

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Vol. 19, no. 4

37--55

EN

Research on document image analysis is actively pursued in the last few decades and services like OCR, vectorization of drawings/graphics and various types of form processing are very common. Handwritten documents, old historical documents and documents captured through camera are now being the subjects of active research. However, another very important type of paper document, namely the map document image processing research suffers due to the inherent complexities of the map document and also for nonavailability of benchmark public data-sets. This paper presents a new data-set, namely, the Land Map Image Database (LMIDb) that consists of a variety of land maps images (446 images at present and growing; scanned at 200/300 dpi in TIF format) and the corresponding ground-truth. Using semiautomatic tools non-text part of the images are deleted and the text-only ground-truth is also kept in the database. This paper also presents a classification strategy for map images using which the maps in the database are automatically classified into Political (Po), Physical (Ph), Resource (R) and Topographic (T) maps. The automatic classification of maps help indexing of the images in LMIDb for archival and easy retrieval of the right maps to get the appropriate geographical information. Classification accuracy is also tested on the proposed data-set and the result is encouraging.

3

Geohraphic map image interpretation : survey and problems

Stąpor K.

Machine Graphics and Vision

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2000

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Vol. 9, No. 1/2

497-518

EN

This paper is brief survey of the most common large scale (1:500-1:1000) geographic map processing techniques in the traditional three levels of document image analysis: pixel-level processing, feature-level analysis and graphic/text recognition and interpretation, followed by a description of some of the most relevant systems developed for map interpretation. The systems (methods) are classifield depending on the type of map they can interpret.

4

A knowledge-based framework for geographic map image analysis

Stąpor K.

Machine Graphics and Vision

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2000

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Vol. 9, No. 3

647-671

EN

In this paper a flexible and generally applicable knowledge--based framework for large-scale geographic map image analysis is proposed. The described framework is composed of three schemas, namely: map model, object detectors and image analysis flow scheme. A new, efficient hybrid knowledge representation scheme, and a flaxible, mixed control strategy based on reasoning with incomplete information are proposed. To increase reliability and accuracy of map image analysis, accumulation of information from defferent sources is used. The proposed approach has been implemented in the map conversion module MAPIN of the Integrated Spatial Organization System SIT, [20].