Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview

Delalandre, Matthieu; Valveny, Ernest; Lladós, Josep

doi:10.1109/das.2008.63

Cited by 13 publications

(5 citation statements)

References 51 publications

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“…Figure 5 shows a typical floor plan, along with all 16 architectural symbol models, from SESYD. An overview of the use of SESYD can be found in [70]. As SESYD is a synthetic dataset, its authors proposed a generation tool to build synthetic documents that sets several constraints to determine various positions and locations of a given vocabulary in different ways over the same set of architectural backgrounds (building structure).…”

Section: Symbol Spotting: Performance Evaluationmentioning

confidence: 99%

Symbol spotting for architectural drawings: state-of-the-art and new industry-driven developments

Rezvanifar

Cote

Albu

2019

IPSJ T Comput Vis Appl

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This review paper offers a contemporary literature survey on symbol spotting in architectural drawing images. Research on isolated symbol recognition is quite mature; the same cannot be said for recognizing a symbol in context. One important challenge is the segmentation/recognition paradox: a system should segment symbols before recognizing them, but some kind of recognition may be necessary to obtain a correct segmentation. Research has thus been recently directed toward symbol spotting, a way of locating possible symbol instances without using full recognition methods. In this paper, we thoroughly review symbol spotting methods with a focus on architectural drawings, an application domain providing the document image analysis and graphic recognition communities with an interesting set of challenges linked to the sheer complexity and density of embedded information, that have yet to be resolved. While most existing methods perform well in terms of recall, their performance is rather poor in terms of precision and false positives. In light of the review, we also propose a simple yet effective symbol spotting method based on template matching and a novel clutter-tolerant cross-correlation function that achieves state-of-the-art results with high precision, high recall, and few false positives, able to cope with “real-life clutter” found in industry-standard architectural drawings.

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Section: Symbol Spotting: Performance Evaluationmentioning

confidence: 99%

Symbol spotting for architectural drawings: state-of-the-art and new industry-driven developments

Rezvanifar

Cote

Albu

2019

IPSJ T Comput Vis Appl

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“…Several comprehensive surveys [8] review the existing works of symbol recognition on logical diagrams, engineering drawings and maps. Comparative results have been reported throughout a series of symbol recognition contests, which concern with the aspects of performance evaluation [3]. Over the past decade, the interest has moved towards the symbol spotting problem.…”

Section: Introductionmentioning

confidence: 99%

Robust Symbol Localization Based on Junction Features and Efficient Geometry Consistency Checking

Pham¹,

Delalandre²,

Barrat³

et al. 2013

2013 12th International Conference on Document Analysis and Recognition

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This paper presents a new approach for symbol localization in line-drawing images using junction features and geometry consistency checking. The proposed system first detects junction points, and then characterizes them by very compact, distinctive, and varying-length descriptors. The detected junctions are used to decompose a document image into a set of smooth primitives composing of isolated shapes (e.g., isolated circles and straight lines) and curve segments bounded between either two junctions or a junction and an end-point. These primitives are then associated with a new set of keypoints to form a complete and compact representation of a graphical document. Next, keypoint matching is performed to find the correspondences among the keypoints of the query and those of database documents. The obtained matches are finally refined by a new and efficient algorithm to deal with the problem of geometry consistency checking. Our experiments showed that the proposed system is very time-and memory-efficient, and provides high accuracy rate of symbol localization.

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“…Symbol recognition plays an important role in drawing understanding [1], especially in the engineering drawings widely used in the machinery, construction, and electronics industries. As a branch of document analysis [2], symbol recognition has achieved great success [3], and international contests for symbol recognition [4] and performance evaluation of symbol recognition systems [5,6] are becoming popular. However, most methods focus on paper-based drawings [7] whereas electronic drawings are now more widely used.…”

Section: Introductionmentioning

confidence: 99%

Strict constraints on 2D primitive pairs for engineering symbol recognition: Theory and application

Qiu

Huang

et al. 2012

Sci. China Inf. Sci.

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Geometric constraints on the line-line pair, line-arc pair and arc-arc pair are proposed. The constraints are able to encode the geometric relations between lines, arcs and circles. A geometric proof is presented to demonstrate that the constraints are stable under translation, rotation, uniform scaling or reflection of the primitive pairs. Based on the constraints, an algorithm for recognizing symbols in drawings is developed. Experimental results show that, compared with three other methods, our method can distinguish more primitive pairs and has higher accuracy in engineering symbol recognition.

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Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview

Cited by 13 publications

References 51 publications

Symbol spotting for architectural drawings: state-of-the-art and new industry-driven developments

Symbol spotting for architectural drawings: state-of-the-art and new industry-driven developments

Robust Symbol Localization Based on Junction Features and Efficient Geometry Consistency Checking

Strict constraints on 2D primitive pairs for engineering symbol recognition: Theory and application

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