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Alicia Fornes, Josep Llados and Gemma Sanchez. 2005. Primitive Segmentation in Old Handwritten Music Scores.
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J. Chazalon, Marçal Rusiñol and Jean-Marc Ogier. 2015. Improving Document Matching Performance by Local Descriptor Filtering. 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015.1216–1220.
Abstract: In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.
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Suman Ghosh, Lluis Gomez, Dimosthenis Karatzas and Ernest Valveny. 2015. Efficient indexing for Query By String text retrieval. 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015.1236–1240.
Abstract: This paper deals with Query By String word spotting in scene images. A hierarchical text segmentation algorithm based on text specific selective search is used to find text regions. These regions are indexed per character n-grams present in the text region. An attribute representation based on Pyramidal Histogram of Characters (PHOC) is used to compare text regions with the query text. For generation of the index a similar attribute space based Pyramidal Histogram of character n-grams is used. These attribute models are learned using linear SVMs over the Fisher Vector [1] representation of the images along with the PHOC labels of the corresponding strings.
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Veronica Romero, Emilio Granell, Alicia Fornes, Enrique Vidal and Joan Andreu Sanchez. 2019. Information Extraction in Handwritten Marriage Licenses Books. 5th International Workshop on Historical Document Imaging and Processing.66–71.
Abstract: Handwritten marriage licenses books are characterized by a simple structure of the text in the records with an evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. Previous works have shown that the use of category-based language models and a Grammatical Inference technique known as MGGI can improve the accuracy of these
tasks. However, the application of the MGGI algorithm requires an a priori knowledge to label the words of the training strings, that is not always easy to obtain. In this paper we study how to automatically obtain the information required by the MGGI algorithm using a technique based on Confusion Networks. Using the resulting language model, full handwritten text recognition and information extraction experiments have been carried out with results supporting the proposed approach.
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Agnes Borras and Josep Llados. 2009. Corest: A measure of color and space stability to detect salient regions according to human criteria. 5th International Conference on Computer Vision Theory and Applications.204–209.
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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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Jon Almazan, Ernest Valveny and Alicia Fornes. 2011. Deforming the Blurred Shape Model for Shape Description and Recognition. In Jordi Vitria, Joao Miguel Raposo and Mario Hernandez, eds. 5th Iberian Conference on Pattern Recognition and Image Analysis. Berlin, Springer-Verlag, 1–8. (LNCS.)
Abstract: This paper presents a new model for the description and recognition of distorted shapes, where the image is represented by a pixel density distribution based on the Blurred Shape Model combined with a non-linear image deformation model. This leads to an adaptive structure able to capture elastic deformations in shapes. This method has been evaluated using thee different datasets where deformations are present, showing the robustness and good performance of the new model. Moreover, we show that incorporating deformation and flexibility, the new model outperforms the BSM approach when classifying shapes with high variability of appearance.
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Lluis Pere de las Heras and Gemma Sanchez. 2011. And-Or Graph Grammar for Architectural Floorplan Representation, Learning and Recognition. A Semantic, Structural and Hierarchical Model. 5th Iberian Conference on Pattern Recognition and Image Analysis.17–24.
Abstract: This paper presents a syntactic model for architectural floor plan interpretation. A stochastic image grammar over an And-Or graph is inferred to represent the hierarchical, structural and semantic relations between elements of all possible floor plans. This grammar is augmented with three different probabilistic models, learnt from a training set, to account the frequency of that relations. Then, a Bottom-Up/Top-Down parser with a pruning strategy has been used for floor plan recognition. For a given input, the parser generates the most probable parse graph for that document. This graph not only contains the structural and semantic relations of its elements, but also its hierarchical composition, that allows to interpret the floor plan at different levels of abstraction.
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Anjan Dutta, Josep Llados and Umapada Pal. 2011. A Bag-of-Paths Based Serialized Subgraph Matching for Symbol Spotting in Line Drawings. In Jordi Vitria, Joao Miguel Raposo and Mario Hernandez, eds. 5th Iberian Conference on Pattern Recognition and Image Analysis. Berlin, Springer Berlin Heidelberg, 620–627. (LNCS.)
Abstract: In this paper we propose an error tolerant subgraph matching algorithm based on bag-of-paths for solving the problem of symbol spotting in line drawings. Bag-of-paths is a factorized representation of graphs where the factorization is done by considering all the acyclic paths between each pair of connected nodes. Similar paths within the whole collection of documents are clustered and organized in a lookup table for efficient indexing. The lookup table contains the index key of each cluster and the corresponding list of locations as a single entry. The mean path of each of the clusters serves as the index key for each table entry. The spotting method is then formulated by a spatial voting scheme to the list of locations of the paths that are decided in terms of search of similar paths that compose the query symbol. Efficient indexing of common substructures helps to reduce the computational burden of usual graph based methods. The proposed method can also be seen as a way to serialize graphs which allows to reduce the complexity of the subgraph isomorphism. We have encoded the paths in terms of both attributed strings and turning functions, and presented a comparative results between them within the symbol spotting framework. Experimentations for matching different shape silhouettes are also reported and the method has been proved to work in noisy environment also.
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David Fernandez, Josep Llados and Alicia Fornes. 2011. Handwritten Word Spotting in Old Manuscript Images Using a Pseudo-Structural Descriptor Organized in a Hash Structure. In Jordi Vitria, Joao Miguel Raposo and Mario Hernandez, eds. 5th Iberian Conference on Pattern Recognition and Image Analysis.628–635.
Abstract: There are lots of historical handwritten documents with information that can be used for several studies and projects. The Document Image Analysis and Recognition community is interested in preserving these documents and extracting all the valuable information from them. Handwritten word-spotting is the pattern classification task which consists in detecting handwriting word images. In this work, we have used a query-by-example formalism: we have matched an input image with one or multiple images from handwritten documents to determine the distance that might indicate a correspondence. We have developed an approach based in characteristic Loci Features stored in a hash structure. Document images of the marriage licences of the Cathedral of Barcelona are used as the benchmarking database.
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