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Palaiahnakote Shivakumara, Anjan Dutta, Trung Quy Phan, Chew Lim Tan and Umapada Pal. 2011. A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video. PR, 44(8), 1671–1683.
Abstract: In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels.
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Partha Pratim Roy, Eduard Vazquez, Josep Llados, Ramon Baldrich and Umapada Pal. 2007. A System to Retrieve Text/Symbols from Color Maps using Connected Component and Skeleton Analysis. In J. Llados, W.L., J.M. Ogier, ed. Seventh IAPR International Workshop on Graphics Recognition.79–78.
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Partha Pratim Roy, Eduard Vazquez, Josep Llados, Ramon Baldrich and Umapada Pal. 2008. A System to Segment Text and Symbols from Color Maps. Graphics Recognition. Recent Advances and New Opportunities.245–256. (LNCS.)
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Partha Pratim Roy and Josep Llados. 2008. Multi-Oriented Character Recognition from Graphical Documents. 2nd International Conference on Cognition and Recognition.30–35.
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Partha Pratim Roy, Josep Llados and Umapada Pal. 2007. Text/Graphics Separation in Color Maps. International Conference on Computing: Theory and Applications.545–551.
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Partha Pratim Roy, Josep Llados and Umapada Pal. 2009. A Complete System for Detection and Recognition of Text in Graphical Documents using Background Information. 5th International Conference on Computer Vision Theory and Applications.
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Partha Pratim Roy, Umapada Pal and Josep Llados. 2008. Multi-oriented English Text Line Extraction using Background and Foreground Information. Proceedings of the 8th IAPR International Workshop on Document Analysis Systems,.315–322.
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Partha Pratim Roy, Umapada Pal and Josep Llados. 2008. Morphology Based Handwritten Line Segmentation using Foreground and Background Information. International Conference on Frontiers in Handwriting Recognition,.241–246.
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Partha Pratim Roy, Umapada Pal and Josep Llados. 2008. Recognition of Multi-oriented Touching Characters in Graphical Documents. Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on,.297–304.
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Partha Pratim Roy, Umapada Pal and Josep Llados. 2009. Seal detection and recognition: An approach for document indexing. 10th International Conference on Document Analysis and Recognition.101–105.
Abstract: Reliable indexing of documents having seal instances can be achieved by recognizing seal information. This paper presents a novel approach for detecting and classifying such multi-oriented seals in these documents. First, Hough Transform based methods are applied to extract the seal regions in documents. Next, isolated text characters within these regions are detected. Rotation and size invariant features and a support vector machine based classifier have been used to recognize these detected text characters. Next, for each pair of character, we encode their relative spatial organization using their distance and angular position with respect to the centre of the seal, and enter this code into a hash table. Given an input seal, we recognize the individual text characters and compute the code for pair-wise character based on the relative spatial organization. The code obtained from the input seal helps to retrieve model hypothesis from the hash table. The seal model to which we get maximum hypothesis is selected for the recognition of the input seal. The methodology is tested to index seal in rotation and size invariant environment and we obtained encouraging results.
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