Séverine Dubuisson

From text detection to text segmentation: A unified evaluation scheme

By Stefania Calarasanu, Jonathan Fabrizio, Séverine Dubuisson

2016-10-01

In Proceedings of the 2nd international workshop on robust reading conference (IWRR-ECCV)

Abstract Current text segmentation evaluation protocols are often incapable of properly handling different scenarios (broken/merged/partial characters). This leads to scores that incorrectly reflect the segmentation accuracy. In this article we propose a new evaluation scheme that overcomes most of the existent drawbacks by extending the EvaLTex protocol (initially designed to evaluate text detection at region level). This new unified platform has numerous advantages: it is able to evaluate a text understanding system at every detection stage and granularity level (paragraph/line/word and now character) by using the same metrics and matching rules; it is robust to all segmentation scenarios; it provides a qualitative and quantitative evaluation and a visual score representation that captures the whole behavior of a segmentation algorithm.

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TextCatcher: A method to detect curved and challenging text in natural scenes

By Jonathan Fabrizio, Myriam Robert-Seidowsky, Séverine Dubuisson, Stefania Calarasanu, Raphaël Boissel

2016-04-08

In International Journal on Document Analysis and Recognition

Abstract In this paper, we propose a text detection algorithm which is hybrid and multi-scale. First, it relies on a connected component-based approach: After the segmentation of the image, a classification step using a new wavelet descriptor spots the letters. A new graph modeling and its traversal procedure allow to form candidate text areas. Second, a texture-based approach discards the false positives. Finally, the detected text areas are precisely cut out and a new binarization step is introduced.

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Towards the rectification of highly distorted texts

By Stefania Calarasanu, Séverine Dubuisson, Jonathan Fabrizio

2016-02-01

In Proceedings of the 11th international conference on computer vision theory and applications (VISAPP)

Abstract A frequent challenge for many Text Understanding Systems is to tackle the variety of text characteristics in born-digital and natural scene images to which current OCRs are not well adapted. For example, texts in perspective are frequently present in real-word images, but despite the ability of some detectors to accurately localize such text objects, the recognition stage fails most of the time. Indeed, most OCRs are not designed to handle text strings in perspective but rather expect horizontal texts in a parallel-frontal plane to provide a correct transcription.

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What is a good evaluation protocol for text localization systems? Concerns, arguments, comparisons and solutions

By Stefania Calarasanu, Jonathan Fabrizio, Séverine Dubuisson

2016-02-01

In Image and Vision Computing

Abstract A trustworthy protocol is essential to evaluate a text detection algorithm in order to, first measure its efficiency and adjust its parameters and, second to compare its performances with those of other algorithms. However, current protocols do not give precise enough evaluations because they use coarse evaluation metrics, and deal with inconsistent matchings between the output of detection algorithms and the ground truth, both often limited to rectangular shapes. In this paper, we propose a new evaluation protocol, named EvaLTex, that solves some of the current problems associated with classical metrics and matching strategies.

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Using histogram representation and earth mover’s distance as an evaluation tool for text detection

By Stefania Calarasanu, Jonathan Fabrizio, Séverine Dubuisson

2015-08-01

In Proceedings of the 13th IAPR international conference on document analysis and recognition (ICDAR)

Abstract In the context of text detection evaluation, it is essential to use protocols that are capable of describing both the quality and the quantity aspects of detection results. In this paper we propose a novel visual representation and evaluation tool that captures the whole nature of a detector by using histograms. First, two histograms (coverage and accuracy) are generated to visualize the different characteristics of a detector. Secondly, we compare these two histograms to a so called optimal one to compute representative and comparable scores.

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A self-adaptive likelihood function for tracking with particle filter

By Séverine Dubuisson, Myriam Robert-Seidowsky, Jonathan Fabrizio

2015-03-01

In Proceedings of the 10th international conference on computer vision theory and applications (VISAPP)

Abstract The particle filter is known to be efficient for visual tracking. However, its parameters are empirically fixed, depending on the target application, the video sequences and the context. In this paper, we introduce a new algorithm which automatically adjusts “on-line" two majors of them: the correction and the propagation parameters. Our purpose is to determine, for each frame of a video, the optimal value of the correction parameter and to adjust the propagation one to improve the tracking performance.

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TextTrail: A robust text tracking algorithm in wild environments

By Myriam Robert-Seidowsky, Jonathan Fabrizio, Séverine Dubuisson

2015-03-01

In Proceedings of the 10th international conference on computer vision theory and applications (VISAPP)

Abstract In this paper, we propose TextTrail, a robust new algorithm dedicated to text tracking in uncontrolled environments (strong motion of camera and objects, partial occlusions, blur, etc.). It is based on a particle filter framework whose correction step has been improved. First, we compare some likelihood functions and introduce a new one that integrates tangent distance. We show that the likelihood function has a strong influence on the text tracking performances.

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Motion compensation based on tangent distance prediction for video compression

By Jonathan Fabrizio, Séverine Dubuisson, Dominique Béréziat

2012-02-09

In Signal Processing: Image Communication

Abstract We present a new algorithm for motion compensation that uses a motion estimation method based on tangent distance. The method is compared with a Block-Matching based approach in various common situations. Whereas Block-Matching algorithms usually only predict positions of blocks over time, our method also predicts the evolution of pixels into these blocks. The prediction error is then drastically decreased. The method is implemented into the Theora codec proving that this algorithm improves the video codec performances.

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