Text detection and recognition in urban scenes

Minetto, Rodrigo; Thome, Nicolas; Cord, Matthieu; Stolfi, Jorge; Precioso, Fŕed́eric; Guyomard, J.; Leite, Neucimar J.

doi:10.1109/iccvw.2011.6130247

Cited by 28 publications

(18 citation statements)

References 12 publications

(30 reference statements)

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“…Overlay text detection schemes can be categorized as either patch based or geometrical property based [1]. Patch based techniques extract features from image patches and identify text regions using pre-trained classifiers [2]. These patches are grouped further to detect the text regions.…”

Section: Introductionmentioning

confidence: 99%

Overlay text extraction from TV news broadcast

Kannao

Guha

2015

2015 Annual IEEE India Conference (INDICON)

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The text data present in overlaid bands convey brief descriptions of news events in broadcast videos. The process of text extraction becomes challenging as overlay text is presented in widely varying formats and often with animation effects. We note that existing edge density based methods are well suited for our application on account of their simplicity and speed of operation. However, these methods are sensitive to thresholds and have high false positive rates. In this paper, we present a contrast enhancement based preprocessing stage for overlay text detection and a parameter free edge density based scheme for efficient text band detection. The second contribution of this paper is a novel approach for multiple text region tracking with a formal identification of all possible detection failure cases. The tracking stage enables us to establish the temporal presence of text bands and their linking over time. The third contribution is the adoption of Tesseract OCR for the specific task of overlay text recognition using web news articles. The proposed approach is tested and found superior on news videos acquired from three Indian English television news channels along with benchmark datasets.

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Section: Introductionmentioning

confidence: 99%

Overlay text extraction from TV news broadcast

Kannao

Guha

2015

2015 Annual IEEE India Conference (INDICON)

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“…Edge-based methods are effective when the background is relatively plain; however, in real-world scenes, strong edges are very common so extra effort should be devoted to reducing false positives. For example, inspired by face and pedestrian detectors [23], Histogram of Oriented Gradients (HOG) based approaches include edge orientation or spatial information, and perform multi-scale character detection via sliding window classification [2]- [4]. However, due to the large search space, at present HOG-based approaches are too computationally expensive for real-time implementation.…”

Section: A Environmental Text Spottingmentioning

confidence: 99%

Bridging text spotting and SLAM with junction features

Wang

Finn

Paull

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Navigating in a previously unknown environment and recognizing naturally occurring text in a scene are two important autonomous capabilities that are typically treated as distinct. However, these two tasks are potentially complementary, (i) scene and pose priors can benefit text spotting, and (ii) the ability to identify and associate text features can benefit navigation accuracy through loop closures. Previous approaches to autonomous text spotting typically require significant training data and are too slow for real-time implementation. In this work, we propose a novel high-level feature descriptor, the "junction", which is particularly well-suited to text representation and is also fast to compute. We show that we are able to improve SLAM through text spotting on datasets collected with a Google Tango, illustrating how location priors enable improved loop closure with text features.

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“…The motivating application for text classifiers such as T-HOG and R-HOG is the detection of text in photos and videos of arbitrary scenes [29,30]. Specifically, the idea is to use the classifier to filter the output of a fast but "permissive" (high-recall and moderate-precision) detector.…”

Section: T-hog As a Post-filter To Text Detectionmentioning

confidence: 99%

T-HOG: An effective gradient-based descriptor for single line text regions

Minetto

Thome

Cord

et al. 2013

Pattern Recognition

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We discuss the use of histogram of oriented gradients (HOG) descriptors as an effective tool for text description and recognition. Specifically, we propose a HOG-based texture descriptor (T-HOG) that uses a partition of the image into overlapping horizontal cells with gradual boundaries, to characterize single-line texts in outdoor scenes. The input of our algorithm is a rectangular image presumed to contain a single line of text in Roman-like characters. The output is a relatively short descriptor, that provides an effective input to an SVM classifier. Extensive experiments show that the T-HOG is more accurate than Dalal and Triggs's original HOG-based classifier, for any descriptor size. In addition, we show that the T-HOG is an effective tool for text/non-text discrimination and can be used in various text detection applications. In particular, combining T-HOG with a permissive bottom-up text detector is shown to outperform state-of-the-art text detection systems in two major publicly available databases.

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Text detection and recognition in urban scenes

Cited by 28 publications

References 12 publications

Overlay text extraction from TV news broadcast

Overlay text extraction from TV news broadcast

Bridging text spotting and SLAM with junction features

T-HOG: An effective gradient-based descriptor for single line text regions

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