HMM-based sliding video text recognition for Turkish broadcast news

Som, Temucin; Can, Doğan; Saraçlar, Murat

doi:10.1109/iscis.2009.5291877

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Negishi et al proposed instead to use corners and curves that are matched relying on a voting algorithm [29]. Recently, inspired by speech recognition, Som et al [39] designed an OCR system that uses an Hidden Markov Model (HMM) to identify characters as a sequence of states. However, as in any pattern recognition problem, the major issue is to define the robust features that represent characters independently of the image resolution and the background complexity.…”

Section: Character Recognitionmentioning

confidence: 99%

Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

Elagouni

García

Mamalet

et al. 2013

IJDAR

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Text embedded in multimedia documents represents an important semantic information that helps to automatically access the content. This paper proposes two neural-based OCRs that handle the text recognition problem in different ways. The first approach segments a text image into individual characters before recognizing them, while the second one avoids the segmentation step by integrating a multi-scale scanning scheme that allows to jointly localize and recognize characters at each position and scale. Some linguistic knowledge is also incorporated into the proposed schemes to remove errors due to recognition confusions. Both OCR systems are applied to caption texts embedded in videos and in natural scene images and provide outstanding results showing that the proposed approaches outperform the state-of-the-art methods.

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Section: Character Recognitionmentioning

confidence: 99%

Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

Elagouni

García

Mamalet

et al. 2013

IJDAR

View full text Add to dashboard Cite

show abstract

“…In [24], after extracting character features, a Bayesian framework is used to combine various information and recognize characters. Recently, inspired by speech recognition, Som et al [20] defined an OCR system that uses a Hidden Markov Model to identify characters as a sequence of states. However, as in any pattern recognition problem, the major issue of these methods is to determine the discriminant features to represent the characters.…”

Section: Related Workmentioning

confidence: 99%

A comprehensive neural-based approach for text recognition in videos using natural language processing

Elagouni

García

Sébillot

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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This work aims at helping multimedia content understanding by deriving benefit from textual clues embedded in digital videos. For this, we developed a complete video Optical Character Recognition system (OCR), specifically adapted to detect and recognize embedded texts in videos. Based on a neural approach, this new method outperforms related work, especially in terms of robustness to style and size variabilities, to background complexity and to low resolution of the image. A language model that drives several steps of the video OCR is also introduced in order to remove ambiguities due to a local letter by letter recognition and to reduce segmentation errors. This approach has been evaluated on a database of French TV news videos and achieves an outstanding character recognition rate of 95%, corresponding to 78% of words correctly recognized, which enables its incorporation into an automatic video indexing and retrieval system.

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Uyghur Text Matching in Graphic Images for Biomedical Semantic Analysis

et al. 2018

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How to read Uyghur text from biomedical graphic images is a challenge problem due to the complex layout and cursive writing of Uyghur. In this paper, we propose a system that extracts text from Uyghur biomedical images, and matches the text in a specific lexicon for semantic analysis. The proposed system possesses following distinctive properties: first, it is an integrated system which firstly detects and crops the Uyghur text lines using a single fully convolutional neural network, and then keywords in the lexicon are matched by a well-designed matching network. Second, to train the matching network effectively an online sampling method is applied, which generates synthetic data continually. Finally, we propose a GPU acceleration scheme for matching network to match a complete Uyghur text line directly rather than a single window. Experimental results on benchmark dataset show our method achieves a good performance of F-measure 74.5%. Besides, our system keeps high efficiency with 0.5s running time for each image due to the GPU acceleration scheme.

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HMM-based sliding video text recognition for Turkish broadcast news

Cited by 6 publications

References 8 publications

Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

A comprehensive neural-based approach for text recognition in videos using natural language processing

Uyghur Text Matching in Graphic Images for Biomedical Semantic Analysis

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