Interpretation of telephone system manhole drawings

Arias, Juan F.; Lai, Chan Pyng; Surya, S.; Kasturi, Rangachar; Chhabra, Atul K.

doi:10.1016/0167-8655(94)00111-f

Cited by 24 publications

(12 citation statements)

References 2 publications

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“…Thus, symbol recognition is usually formulated in terms of detection of polygonal shapes and hatched patterns. Another subdomain of map interpretation focus on utility maps [53,54,39]. Utility maps contain information on network facilities of companies such as water, telephone, gas, etc.…”

Section: Mapsmentioning

confidence: 99%

Symbol Recognition: Current Advances and Perspectives

Lladós¹,

Valveny²,

Sánchez³

et al. 2002

Lecture Notes in Computer Science

102

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Abstract. 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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Section: Mapsmentioning

confidence: 99%

Symbol Recognition: Current Advances and Perspectives

Lladós¹,

Valveny²,

Sánchez³

et al. 2002

Lecture Notes in Computer Science

102

View full text Add to dashboard Cite

show abstract

“…A majority of works employed knowledge-based approaches (Arias et al 1993) to perform a symbol extraction based on set of rules. Other approaches identify the symbols in the legend, constructing a training set library to then use it in the classification of the geographic symbols (Samet and Soffer 1998).…”

Section: Existing Features Extraction Methodsmentioning

confidence: 99%

Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space

Herrault

Fauvel

Paegelow

2013

Geographic Information Science at the Heart of Europe

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In this chapter, we describe an automatic procedure to capture features on old maps. Early maps contain specific informations which allow us to reconstruct trajectories over time and space for land use/cover studies or urban area development. The most commonly used approach to extract these elements requires a user intervention for digitizing which widely limits its utilization. Therefore, it is essential to propose automatic methods in order to establish reproducible procedures. Capturing features automatically on scanned paper maps is a major challenge in GIS for many reasons: (1) many planimetric elements can be overlapped, (2) scanning procedure may conduct to a poor image quality, (3) lack of colors complicates the distinction of the elements. Based on a state of art, we propose a method based on color image segmentation and unsupervised classification (K-means algorithm) to extract forest features on the historical 'Map of France'. The first part of the procedure conducts to clean maps and eliminate elevation contour lines with filtering techniques. Then, we perform a color space conversion from RGB to L*a*b color space to improve uniformity of the image. To finish, a post processing step based on morphological operators and contextual rules is applied to clean-up features. Results show a high global accuracy of the proposed scheme for different excerpt of this historical map.

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“…How is the interpretation strategy implemented or guided? The interpretation strategy can be completely hard-wired in the case of bottom-up approaches, but it can also be guided by a blackboard in the context of centralized reasoning process [Vaxivière 1995], it can be distributed in multi-agent systems [Ogier 2001Grenier 2000, Arias 1995. Furthermore, the reasoning process handling the interpretation strategy can either be implemented as predefined interpretation scenarii (which often rely on bottom up strategies), or, on the other hand, rely on opportunistic approaches based on progressive assessment of the knowledge which is produced by the different modules of the system.…”

Section: Classification Of Graphical Document Analysis Systemsmentioning

confidence: 99%

Analysis and Interpretation of Graphical Documents

Ogier

2014

Handbook of Document Image Processing and Recognition

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This chapter is dedicated to the analysis and the interpretation of graphical documents, and as such, builds upon many of the topics covered in other parts of this handbook. It will therefore not focus on any of the technical issues related to graphical documents, such as low level filtering and binarization, primitive extraction and vectorization as developed in Chapters 2.1 and 5.1 or symbol recognition, for instance, as developed in Chapter 5.2. These tools are put in a broader framework and threaded together in complex pipelines to solve interpretation questions. This chapter provides an overview of how analysis strategies have contributed to constructing these pipelines, how specific domain knowledge is integrated in these analyses, and which interpretation contexts have been contributed to successful approaches.

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Interpretation of telephone system manhole drawings

Cited by 24 publications

References 2 publications

Symbol Recognition: Current Advances and Perspectives

Symbol Recognition: Current Advances and Perspectives

Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space

Analysis and Interpretation of Graphical Documents

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