Support vector machine-based text detection in digital video

Shin, Chang-Hoon; Kim, K.I.; Park, M.H.; Kim, H.J.

doi:10.1109/nnsp.2000.890142

Cited by 32 publications

(12 citation statements)

References 4 publications

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“…Precision ratio is 81.2%, extraction ratio 83.6%. As is shown in Table 1 above, compared with the reference [2] and reference [11] [14], Precision ratio and extraction ratio of the method proposed by this paper has obvious improvement. As shown in figure 10, our method located the text areas correctly.…”

Section: Experimental Results and Valuationmentioning

confidence: 71%

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A New Approach for Text Location Based on SUSAN and SVM

Wang¹,

Zhou²

2013

2013 International Conference on Information Science and Cloud Computing Companion

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In order to extract text parts from images, this paper proposes a new approach that combines edge detection, heuristic knowledge and SVM. Firstly, SUSAN algorithm with an adaptive threshold is used to extract the edge information from gray images, and the binary edge images are obtained; Secondly, the heuristic knowledge is used to filter the text candidate areas obtained by edge dilation, and the positions of the text candidate areas in the binary image apply to the gray image; Finally we use statistics of gray level co-occurrence matrix as SVM's inputs to construct SVM which is used to verify the text areas further. The experimental results show that the proposed approach can effectively locate the text areas with high accurate and extraction ratios.

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Section: Experimental Results and Valuationmentioning

confidence: 71%

“…SVM is a machine learning method that can be trained, which expresses the data set that needs classifying as feature vectors of multidimensional space, and constructs the optimal margin hyperplane in the feature space to make the isolation between positive and negative cases maximal [11].…”

Section: Filtrate Of Text Areasmentioning

confidence: 99%

A New Approach for Text Location Based on SUSAN and SVM

Wang¹,

Zhou²

2013

2013 International Conference on Information Science and Cloud Computing Companion

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“…Generally, these features are extracted by scanning the image with a small window which are then fed to the classifier. Calssifiers like support vector machine (SVM) and artificial neural networks (ANN) have been extensively applied for this purpose [26], [27], [28], [29], [30], [31]. In some cases, coarse-to-fine algorithms have also been evaluated where the candidate text pixels are first identified and then valiated by a classifer [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Multilingual Artificial Text Extraction and Script Identification from Video Images

Jamıl¹,

Batool²,

Malik³

et al. 2016

ijacsa

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Abstract-This work presents a system for extraction and script identification of multilingual artificial text appearing in video images. As opposed to most of the existing text extraction systems which target textual occurrences in a particular script or language, we have proposed a generic multilingual text extraction system that relies on a combination of unsupervised and supervised techniques. The unsupervised approach is based on application of image analysis techniques which exploit the contrast, alignment and geometrical properties of text and identify candidate text regions in an image. Potential text regions are then validated by an Artificial Neural Network (ANN) using a set of features computed from Gray Level Co-occurrence Matrices (GLCM). The script of the extracted text is finally identified using texture features based on Local Binary Patterns (LBP). The proposed system was evaluated on video images containing textual occurrences in five different languages including English, Urdu, Hindi, Chinese and Arabic. The promising results of the experimental evaluations validate the effectiveness of the proposed system for text extraction and script identification.

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“…However, when the complex background regions in the image have similar color as the text, the test results are not satisfactory. Machine learning based method [6] classifies text blocks and nontext blocks by constructing the mechanism of learning. Since such methods need to select samples in order to train the learning machine for classification [7], the similarity between training sample sets and test sample sets is not high enough to perform ideal detection results.…”

Section: Introductionmentioning

confidence: 99%

A Method of Effective Text Extraction for Complex Video Scene

Guo

Yuan

Wang

et al. 2016

Mathematical Problems in Engineering

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Text information contains important information for video analysis, indexing, and retrieval. Effective and efficient text extraction has been a challenging topic in recent years. Focusing on this issue, a text extraction method for complex video scene is proposed in this paper. Multiframe corner matching and heuristic rules are combined together to detect the text region candidates, which solves the issue of Harris corner filtration for complex video scene and also improves the detection accuracy using multiframe fusion. Local texture description is then used for similarity evaluation judged by SVM. Experimental results for 4 different types of 395-frame video images show the effectiveness of the proposed method compared with 5 existing text extraction methods.

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Support vector machine-based text detection in digital video

Cited by 32 publications

References 4 publications

A New Approach for Text Location Based on SUSAN and SVM

A New Approach for Text Location Based on SUSAN and SVM

Multilingual Artificial Text Extraction and Script Identification from Video Images

A Method of Effective Text Extraction for Complex Video Scene

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