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Andreas Fischer, Volkmar Frinken, Alicia Fornes and Horst Bunke. 2011. Transcription Alignment of Latin Manuscripts Using Hidden Markov Models. Proceedings of the 2011 Workshop on Historical Document Imaging and Processing. ACM, 29–36.
Abstract: Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models.
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Pau Riba, Anjan Dutta, Josep Llados and Alicia Fornes. 2017. Graph-based deep learning for graphics classification. 12th IAPR International Workshop on Graphics Recognition.29–30.
Abstract: Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems
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Joana Maria Pujadas-Mora, Alicia Fornes, Josep Llados, Gabriel Brea-Martinez and Miquel Valls-Figols. 2019. The Baix Llobregat (BALL) Demographic Database, between Historical Demography and Computer Vision (nineteenth–twentieth centuries. Nominative Data in Demographic Research in the East and the West: monograph.29–61.
Abstract: The Baix Llobregat (BALL) Demographic Database is an ongoing database project containing individual census data from the Catalan region of Baix Llobregat (Spain) during the nineteenth and twentieth centuries. The BALL Database is built within the project ‘NETWORKS: Technology and citizen innovation for building historical social networks to understand the demographic past’ directed by Alícia Fornés from the Center for Computer Vision and Joana Maria Pujadas-Mora from the Center for Demographic Studies, both at the Universitat Autònoma de Barcelona, funded by the Recercaixa program (2017–2019).
Its webpage is http://dag.cvc.uab.es/xarxes/.The aim of the project is to develop technologies facilitating massive digitalization of demographic sources, and more specifically the padrones (local censuses), in order to reconstruct historical ‘social’ networks employing computer vision technology. Such virtual networks can be created thanks to the linkage of nominative records compiled in the local censuses across time and space. Thus, digitized versions of individual and family lifespans are established, and individuals and families can be located spatially.
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Manuel Carbonell, Joan Mas, Mauricio Villegas, Alicia Fornes and Josep Llados. 2019. End-to-End Handwritten Text Detection and Transcription in Full Pages. 2nd International Workshop on Machine Learning.29–34.
Abstract: When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.
Keywords: Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning
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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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Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez and Joan Mas. 2008. Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.30–40. (LNCS.)
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Jaume Gibert, Ernest Valveny and Horst Bunke. 2010. Graph of Words Embedding for Molecular Structure-Activity Relationship Analysis. 15th Iberoamerican Congress on Pattern Recognition.30–37. (LNCS.)
Abstract: Structure-Activity relationship analysis aims at discovering chemical activity of molecular compounds based on their structure. In this article we make use of a particular graph representation of molecules and propose a new graph embedding procedure to solve the problem of structure-activity relationship analysis. The embedding is essentially an arrangement of a molecule in the form of a vector by considering frequencies of appearing atoms and frequencies of covalent bonds between them. Results on two benchmark databases show the effectiveness of the proposed technique in terms of recognition accuracy while avoiding high operational costs in the transformation.
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Sounak Dey, Anjan Dutta, Josep Llados, Alicia Fornes and Umapada Pal. 2017. Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario. 12th IAPR International Workshop on Graphics Recognition.31–32.
Abstract: One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration
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Albert Gordo, Florent Perronnin, Yunchao Gong and Svetlana Lazebnik. 2014. Asymmetric Distances for Binary Embeddings. TPAMI, 36(1), 33–47.
Abstract: In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.
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Agnes Borras and Josep Llados. 2007. Similarity-Based Object Retrieval Using Appearance and Geometric Feature Combination. 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120.33–39.
Abstract: This work presents a content-based image retrieval system of general purpose that deals with cluttered scenes containing a given query object. The system is flexible enough to handle with a single image of an object despite its rotation, translation and scale variations. The image content is divided in parts that are described with a combination of features based on geometrical and color properties. The idea behind the feature combination is to benefit from a fuzzy similarity computation that provides robustness and tolerance to the retrieval process. The features can be independently computed and the image parts can be easily indexed by using a table structure on every feature value. Finally a process inspired in the alignment strategies is used to check the coherence of the object parts found in a scene. Our work presents a system of easy implementation that uses an open set of features and can suit a wide variety of applications.
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