Musings on Symbol Recognition

Tombre, Karl; Tabbone, Salvatore; Dosch, Philippe

doi:10.1007/11767978_3

Cited by 19 publications

(11 citation statements)

References 44 publications

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“…The second challenge is that due to the ever-growing size of document image repositories, fast(er) methods are necessary to handle large databases. To overcome these bottlenecks, the research trend has gradually shifted from the traditional concept of symbol recognition toward the newer concept of symbol spotting, a way of locating a given query symbol within a graphical document image without using full recognition methods, while limiting the computational complexity [9]. As a consequence, symbol spotting has experienced a growing interest in the Graphics Recognition community in such a way that the evaluation of symbol spotting approaches was added for the first time in 2011 to the series of IAPR Workshops on Graphics Recognition (GREC) symbol recognition contests [15].…”

Section: Introduction 11 Overviewmentioning

confidence: 99%

Geometry-based symbol spotting in born-digital architectural floor plans

2021

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Architectural floor plans are scale-accurate 2D drawings of one level of a building, seen from above, which convey structural and semantic information related to rooms, walls, symbols, textual data, etc. They consist of lines, curves, symbols, and textual markings, showing the relationships between rooms and all physical features, required for the proper construction or renovation of the building.First, this thesis provides a thorough study of state-of-the-art on symbol spotting methods for 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.Second, we propose a hybrid method that capitalizes on strengths of both vector-based and pixel-based symbol spotting techniques. In the description phase, the salient geometric constituents of a symbol are extracted by a variety of vectorization techniques, including a proposed voting-based algorithm for finding partial ellipses. This enables us to better handle local shape irregularities and boundary discontinuities, as well as partial occlusion and overlap. In the matching phase, the spatial relationship between the geometric primitives is encoded via a primitive-aware proximity graph. A statistical approach is then used to rapidly yield a coarse localization of symbols within the plan. Localization is further refined with a pixel-based step implementing a modified cross-correlation function. Experimental results on the public SESYD synthetic dataset and real-world images demonstrate that our approach clearly outperforms other popular symbol spotting approaches.Traditional on-the-fly symbol spotting methods are unable to address the semantic challenge of graphical notation variability, i.e. low intra-class symbol similarity, an issue that is particularly important in architectural floor plan analysis. The presence of occlusion and clutter, characteristic of real-world plans, along with a varying graphical symbol complexity from almost trivial to highly complex, also pose challenges to existing spotting methods. iv Third, we address all the above issues by leveraging recent advances in deep learningbased neural networks and adapting an object detection framework based on the YOLO (You Only Look Once) architecture. We propose a training strategy based on tiles, avoiding many issues particular to deep learning-based object detection networks related to the relatively small size of symbols compared to entire floor plans, aspect ratios, and data augmentation. Experimental results demonstrate that our method successfully detects architectural symbols with low intra-class similarity and of variable graphical complexity,

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Section: Introduction 11 Overviewmentioning

confidence: 99%

Geometry-based symbol spotting in born-digital architectural floor plans

2021

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“…The research surveys by Chhabra [2], Llados et al [3], Cordella & Vento [4] and Tombre et al [5] provide a detailed and state of the art historical review of work done in the field of symbol recognition over last two decades. Graphic symbol recognition is generally approached by syntactic, structural, statistical or hybrid methods of pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Luqman

Delalandre

Brouard

et al. 2010

Graphics Recognition. Achievements, Challenges, and Evolution

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Abstract. The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols.

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“…Several surveys [8] [11] [31] [47] review existing works on logical diagrams, engineering drawings, maps, etc. In a very general way [31], a symbol can be defined as "a graphical entity with a particular meaning in the context of an specific application domain" and then symbol recognition as "a particular application of the general problem of pattern recognition, in which an unknown input pattern (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Since research on symbol spotting is just starting, it is still a little ambiguous to define "what a spotting method is". In [47] it is defined as "a way to efficiently localize possible symbols and limit the computational complexity, without us- (Figure 1). Spotting is initiated with a query selected from a drawing by the user, what we call a QBE 4 .…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview

Delalandre

Valveny

Lladós

2008

2008 the Eighth IAPR International Workshop on Document Analysis Systems

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This paper deals with the topic of performance evaluation of the symbol recognition & spotting systems. It presents an overview as a result of the work and the discussions undertaken by a working group on this subject. The paper starts by giving a general view of symbol recognition & spotting and performance evaluation. Next, the two main issues of performance evaluation are discussed: groundtruthing and performance characterization. Different problems related to both issues are addressed: groundtruthing of real documents, generation of synthetic documents, degradation models, the use of a priori knowledge, mapping of the groundtruth with the system results, and so on. Open problems arising from this overview are also discussed at the end of the paper.

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Musings on Symbol Recognition

Cited by 19 publications

References 44 publications

Geometry-based symbol spotting in born-digital architectural floor plans

Geometry-based symbol spotting in born-digital architectural floor plans

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview

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