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Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados |
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Title |
Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model |
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2019 |
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Pattern Recognition |
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PR |
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86 |
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27-36 |
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Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks |
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Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches. |
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DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 |
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Admin @ si @ TCF2019 |
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3166 |
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Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce |
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The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces |
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2018 |
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Technology Innovation Management Review |
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DAG; MV; 600.097; 600.121; 600.129;SIAI |
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Admin @ si @ VKV2018a |
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3153 |
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Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam |
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Subgraph spotting in graph representations of comic book images |
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2018 |
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Pattern Recognition Letters |
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PRL |
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112 |
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118-124 |
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Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images |
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Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. |
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DAG; 600.097; 600.121 |
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Admin @ si @ LLD2018 |
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3150 |
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Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
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Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
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2018 |
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Pattern Recognition |
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PR |
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80 |
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64-82 |
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Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
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Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
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Admin @ si @ RSJ2018 |
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3096 |
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Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
Product graph-based higher order contextual similarities for inexact subgraph matching |
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Journal Article |
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2018 |
Publication |
Pattern Recognition |
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PR |
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76 |
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596-611 |
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Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem. |
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DAG; 602.167; 600.097; 600.121 |
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Admin @ si @ DLB2018 |
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3083 |
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