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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 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, 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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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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Palaiahnakote Shivakumara, Anjan Dutta, Chew Lim Tan and Umapada Pal. 2014. Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing. MTAP, 72(1), 515–539.
Abstract: In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well.
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados and Alicia Fornes. 2014. A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance. 22nd International Conference on Pattern Recognition.3074–3079.
Abstract: Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.
Keywords: word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados and Alicia Fornes. 2014. A Novel Learning-free Word Spotting Approach Based on Graph Representation. 11th IAPR International Workshop on Document Analysis and Systems.207–211.
Abstract: Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados and Alicia Fornes. 2014. Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité. Colloque International Francophone sur l'Écrit et le Document.233–248.
Abstract: Effective information retrieval on handwritten document images has always been
a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labeled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment results introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.
Keywords: word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example
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Oriol Vicente, Alicia Fornes and Ramon Valdes. 2016. The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities. Digital Humanities Centres: Experiences and Perspectives.
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