Scene Text Extraction from Videos Using Hybrid Approach

Thilagavathy, A.; Aarthi, K.; Chilambuchelvan, A.

doi:10.1007/978-3-642-31552-7_75

Cited by 2 publications

(2 citation statements)

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“…Obtained results are then refined by a SVM classification. Thilagavathy et al [6] combined binarization for text region extraction and classification with a Multi-Layer Perceptron.…”

Section: Related Workmentioning

confidence: 99%

Arabic text detection in videos using neural and boosting-based approaches: Application to video indexing

Yousfi

Berrani

García

2014

2014 IEEE International Conference on Image Processing (ICIP)

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Text detection in videos is a primary step in any semanticbased video analysis systems. In this work, we propose and compare three machine learning-based methods for embedded Arabic text detection. These methods are able to detect Arabic text regions without any prior knowledge and without any pre-processing. The first method relies on a convolution neural network. The two other methods are based on a multiexit asymmetric boosting cascade. The proposed methods have been extensively evaluated on a large database of Arabic TV channel videos. Experiments highlight a good detection rate of all methods even though neural network-based method outperforms the other ones in terms of recall/precision and computation time.

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“…Obtained results are then refined by a SVM classification. Thilagavathy et al [6] combined binarization for text region extraction and classification with a Multi-Layer Perceptron.…”

Section: Related Workmentioning

confidence: 99%

Arabic text detection in videos using neural and boosting-based approaches: Application to video indexing

Yousfi

Berrani

García

2014

2014 IEEE International Conference on Image Processing (ICIP)

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“…The technique uses kernel temporal segmentation process which allows rejecting the frames with nearly corresponding values which after applying to support vector machines performs summarization of video. Thilagavathy et al [48] have presented a technique that uses neural network for performing extraction of text from multimedia file system. The technique initially obtain key frame from the video where the text region indictor is used along with minimum spanning tree.…”

Section: Existing Techniques Of Vamentioning

confidence: 99%

Insights to Video Analytic Modelling Approach with Future Line of Research

Chandra¹,

Reddy²

2016

IJCA

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The area of Video analytics has made some significant improvement due to advancement in image processing and datamining techniques. However, the inclination is still towards image contents and less to mined contents owing to many unsolved issues. Although concept of mining is more than 2 decade old, but mining approaches are yet to be standardized in the area of video surveillance system. With evolution of newer set of challenges in video capturing, existing mining models finds itself less applicable due to unstructured format of dynamic frames. Hence, this paper discusses about video analytics and presents a brief discussion of frequently used mining approaches in video as well as discussed some recent studies in this direction in order to scale the degree of effectiveness in existing system. The paper also presents research gap and provided solution as future line of research as a possible way to overcome the research gap.

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Scene Text Extraction from Videos Using Hybrid Approach

Cited by 2 publications

References 10 publications

Arabic text detection in videos using neural and boosting-based approaches: Application to video indexing

Arabic text detection in videos using neural and boosting-based approaches: Application to video indexing

Insights to Video Analytic Modelling Approach with Future Line of Research

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