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Tanaporn Pakpoom

'Mobile is the new PC'

Mobile Devices)

PC

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Mobile Devices)
PC

Mobile is the new

Megatrend

“
Mobile Devices

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Mobile Devices
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: http://www.thairath.co.th/content/tech/381992

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Abstract--Text detection and recognition is a hot topic for researchers in the field of image processing and multimedia. Content based Image Retrieval (CBIR) community fills the semantic gap between low-level and high-level features. For text detection and extraction that achieve reasonable accuracy for multi-oriented text and natural scene text (camera images), several methods have been developed. In general most of the methods use classifier and large number of training samples to improve the accuracy in text detection. In general, connected components are used to tackle the multi-orientation problem. The connected component analysis based features with classifier training, work well for achieving better accuracy when the images are highly contrast. However, when the same methods used directly for text detection in video it results in disconnections, loss of shapes etc, because of low contrast and complex background. For such cases, deciding geometrical features of the components and classifier is not that easy. To overcome from this problem the proposed research uses Gradiant Vector Flow and Grouping based Method for Arbitrarily Oriented Scene text Detection method. The GVF of edge pixels in the Sobel edge map of the input frame is explored to identify the dominant edge pixels which represent text components. The method extracts dominant pixel’s edge components corresponding to the Sobel edge map, which is called Text Candidates (TC) of the text lines. Experimental results on different datasets including text data that is oriented arbitrary, non-horizontal text data also horizontal text data, Hua’s data and ICDAR-03 data (Camera images) show that the proposed method outperforms existing methods.

Scene Text Detection of Curved Text Using Gradiant Vector Flow Method

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