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Sounak Dey, Anguelos Nicolaou, Josep Llados and Umapada Pal. 2019. Evaluation of the Effect of Improper Segmentation on Word Spotting. IJDAR, 22, 361–374.
Abstract: Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods.
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Sergi Garcia Bordils and 7 others. 2022. Out-of-Vocabulary Challenge Report. Proceedings European Conference on Computer Vision Workshops.359–375. (LNCS.)
Abstract: This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions.
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Ernest Valveny and Philippe Dosch. 2004. Performance Evaluation of Symbol Recognition. In S. Marinai, A.D.(E.),, ed. Document Analysis Systems.354–365.
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Marçal Rusiñol and Josep Llados. 2017. Flowchart Recognition in Patent Information Retrieval. In M. Lupu, K. Mayer, N. Kando and A.J. Trippe, eds. Current Challenges in Patent Information Retrieval. Springer Berlin Heidelberg, 351–368.
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2008. Writer Identification in Old Handwritten Music Scores. Proceedings of the 8th International Workshop on Document Analysis Systems,.347–353.
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Anton Cervantes, Gemma Sanchez, Josep Llados, Agnes Borras and Ana Rodriguez. 2006. Biometric Recognition Based on Line Shape Descriptors. Lecture Notes in Computer Science. Springer Link, 346–357,.
Abstract: Abstract. In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to iden- tify newborns from the biometric information of the ears are developed. The process steps are the following: image acquisition, ear segmentation, ear normalization, feature extraction and identification. Several shape signatures are defined from contour images. These are formulated in terms of zoning and contour crossings descriptors. Experimental results are presented to demonstrate the effectiveness of the used techniques.
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Miquel Ferrer, Ernest Valveny and F. Serratosa. 2009. Median Graph Computation by means of a Genetic Approach Based on Minimum Common Supergraph and Maximum Common Subraph. 4th Iberian Conference on Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 346–353. (LNCS.)
Abstract: Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present a new genetic algorithm for the median graph computation. A set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
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Emanuele Vivoli, Ali Furkan Biten, Andres Mafla, Dimosthenis Karatzas and Lluis Gomez. 2022. MUST-VQA: MUltilingual Scene-text VQA. Proceedings European Conference on Computer Vision Workshops.345–358. (LNCS.)
Abstract: In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks.
Keywords: Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models
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Miquel Ferrer, Ernest Valveny, F. Serratosa, I. Bardaji and Horst Bunke. 2009. Graph-based k-means clustering: A comparison of the set versus the generalized median graph. 13th International Conference on Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 342–350. (LNCS.)
Abstract: In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.
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Farshad Nourbakhsh, Dimosthenis Karatzas and Ernest Valveny. 2010. A polar-based logo representation based on topological and colour features. 9th IAPR International Workshop on Document Analysis Systems.341–348.
Abstract: In this paper, we propose a novel rotation and scale invariant method for colour logo retrieval and classification, which involves performing a simple colour segmentation and subsequently describing each of the resultant colour components based on a set of topological and colour features. A polar representation is used to represent the logo and the subsequent logo matching is based on Cyclic Dynamic Time Warping (CDTW). We also show how combining information about the global distribution of the logo components and their local neighbourhood using the Delaunay triangulation allows to improve the results. All experiments are performed on a dataset of 2500 instances of 100 colour logo images in different rotations and scales.
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