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• W2608990265 abstract "Text detection techniques assume that the frames given to them are all text frames. When a non-text frame is fed to the text detector there is an increase in the number of false positives which is a major issue in text detection and recognition. Hence, classification of text frames in a video sequence is important. The duplicate frames are eliminated and label each frame as text and non-text before it can be given for detection and recognition of text in a frame. Text in a frame is detected using bounding box technique. Once the text regions are marked, Fuzzy C Means (FCM) algorithm is used to classify each frame as text and non-text. The performance of the proposed work is measured in terms of precision, recall and F-measure and is compared with the existing approaches. The classified text frames are taken as input and are sent to the OCR Engine for character recognition. Several applications such as character recognition, video content analysis, and content based image retrieval, video summarization, and script identification etc., uses text detection." @default.
• W2608990265 created "2017-05-05" @default.
• W2608990265 creator A5063078115 @default.
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• W2608990265 date "2016-01-01" @default.
• W2608990265 modified "2023-09-28" @default.
• W2608990265 title "Text frame classification and recognition using segmentation technique" @default.
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• W2608990265 doi "https://doi.org/10.1109/icatcct.2016.7912083" @default.
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