Report on the Symbol Recognition and Spotting Contest

Valveny, Ernest; Delalandre, Matthieu; Raveaux, Romain

doi:10.1007/978-3-642-36824-0_19

Cited by 20 publications

(27 citation statements)

References 11 publications

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“…Data was recorded from 18 able-bodied participants (age=22.8 ± 3.5, 3 females). Each participant had to write a series of symbols taken from a subset of 30 symbols that were used during the International Symbol Recognition Contest GREC'2011 1 [12,34]. The distance between users and the screen varied between participants.…”

Section: Image Acquisitionmentioning

confidence: 99%

Hand-Drawn Symbol Recognition in Immersive Virtual Reality Using Deep Extreme Learning Machines

Cecotti

Boumedine

Callaghan

2017

Communications in Computer and Information Science

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Typical character and symbol recognition systems are based on images that are drawn on paper or on a tablet with actual physical contact between the pen and the surface. In this study, we investigate the recognition of symbols that are written while the user is immersed inside a room scale virtual reality experience using a consumer grade head-mounted display and related peripherals. A novel educational simulation was developed consisting of a virtual classroom with whiteboard where users can draw symbols. A database of 30 classes of hand-drawn symbols created from test subjects using this environment is presented. The performance of the symbol recognition system was evaluated with deep extreme learning machine classifiers, with accuracy rates of 94.88% with a single classifier and 95.95% with a multiple classifier approach. Further analysis of the results obtained support the conclusion that there are a number of challenges and difficulties related to drawing in this type of environment given the unique constraints and limitations imposed by virtual reality and in particular the lack of physical contact and haptic feedback between the controller and virtual space. Addressing the issues raised for these types of interfaces opens new challenges for both human-computer interaction and symbol recognition. Finally, the approach proposed in this paper creates a new avenue of research in the field of document analysis and recognition by exploring how texts and symbols can be analyzed and automatically recognized in virtual scenes of this type.

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Section: Image Acquisitionmentioning

confidence: 99%

Hand-Drawn Symbol Recognition in Immersive Virtual Reality Using Deep Extreme Learning Machines

Cecotti

Boumedine

Callaghan

2017

Communications in Computer and Information Science

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“…In the following, we illustrate how the inhibition-augmented filter that we propose is effective for such an application. We evaluate the proposed approach on the Graphics Recognition Contest (GREC'11) data set [54]. The GREC'11 data set contains 150 different symbol classes, in which the images are of size 256 × 256 pixels.…”

Section: Recognition Of Architectural and Electrical Symbolsmentioning

confidence: 99%

Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition

Guo

Shi

Azzopardi

et al. 2016

Machine Vision and Applications

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The shape and meaning of an object can radically change with the addition of one or more contour parts. For instance, a T-junction can become a crossover. We extend the COSFIRE trainable filter approach which uses a positive prototype pattern for configuration by adding a set of negative prototype patterns. The configured filter responds to patterns that are similar to the positive prototype but not to any of the negative prototypes. The configuration of such a filter comprises selecting given channels of a bank of Gabor filters that provide excitatory or inhibitory input and determining certain blur and shift parameters. We compute the response of such a filter as the excitatory input minus a fraction of the maximum of inhibitory inputs. We use three applications to demonstrate the effectiveness of inhibition: the exclusive detection of vascular bifurcations (i.e., without crossovers) in retinal fundus images (DRIVE data set), the recognition of architectural and electrical symbols (GREC'11 data set) and the recognition of handwritten digits (MNIST data set).

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“…We use the GREC 2011 data set [16] that contains 150 model architectural and electrical symbols, and three data sets (called NoiseA, NoiseB and NoiseE) of images with different levels of degradation. In each of the three noisy data sets, there are 25 degraded images for every symbol class.…”

Section: Data Setsmentioning

confidence: 99%

“…In Section 2 we explain how a COSFIRE with inhibition is configured and applied. In Section 3 we describe our experiments on four subsets of images from the Graphics Recognition (GREC 2011) data set [16]. We provide a discussion in Section 4 and draw conclusions in Section 5.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Architectural and Electrical Symbols by COSFIRE Filters with Inhibition

Guo

Shi

Azzopardi

et al. 2015

Computer Analysis of Images and Patterns

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Abstract. The automatic recognition of symbols can be used to automatically convert scanned drawings into digital representations compatible with computer aided design software. We propose a novel approach to automatically recognize architectural and electrical symbols. The proposed method extends the existing trainable COSFIRE approach by adding an inhibition mechanism that is inspired by shape-selective TEO neurons in visual cortex. A COSFIRE filter with inhibition takes as input excitatory and inhibitory responses from line and edge detectors. The type (excitatory or inhibitory) and the spatial arrangement of low level features are determined in an automatic configuration step that analyzes two types of prototype pattern called positive and negative. Excitatory features are extracted from a positive pattern and inhibitory features are extracted from one or more negative patterns. In our experiments we use four subsets of images with different noise levels from the Graphics Recognition data set (GREC 2011) and demonstrate that the inhibition mechanism that we introduce improves the effectiveness of recognition substantially.

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Report on the Symbol Recognition and Spotting Contest

Cited by 20 publications

References 11 publications

Hand-Drawn Symbol Recognition in Immersive Virtual Reality Using Deep Extreme Learning Machines

Hand-Drawn Symbol Recognition in Immersive Virtual Reality Using Deep Extreme Learning Machines

Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition

Recognition of Architectural and Electrical Symbols by COSFIRE Filters with Inhibition

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