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Klaus Broelemann, Anjan Dutta, Xiaoyi Jiang and Josep Llados. 2014. Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents. In Bart Lamiroy and Jean-Marc Ogier, eds. Graphics Recognition. Current Trends and Challenges. Springer Berlin Heidelberg, 25–37. (LNCS.)
Abstract: Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset.
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Arnau Baro, Pau Riba, Jorge Calvo-Zaragoza and Alicia Fornes. 2017. Optical Music Recognition by Recurrent Neural Networks. 14th IAPR International Workshop on Graphics Recognition.25–26.
Abstract: Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level
Keywords: Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2007. Old Handwritten Musical Symbol Classification by a Dynamic Time Warping Based Method. Seventh IAPR International Workshop on Graphics Recognition.26–27.
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2012. Writer Identification in Old Handwritten Music Scores. In Copnstantin Papaodysseus, ed. Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology. IGI-Global, 27–63.
Abstract: The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%.
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Adria Rico and Alicia Fornes. 2017. Camera-based Optical Music Recognition using a Convolutional Neural Network. 12th IAPR International Workshop on Graphics Recognition.27–28.
Abstract: Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results
Keywords: optical music recognition; document analysis; convolutional neural network; deep learning
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Juan Ignacio Toledo, Manuel Carbonell, Alicia Fornes and Josep Llados. 2019. Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model. PR, 86, 27–36.
Abstract: 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.
Keywords: Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks
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Albert Suso, Pau Riba, Oriol Ramos Terrades and Josep Llados. 2021. A Self-supervised Inverse Graphics Approach for Sketch Parametrization. 16th International Conference on Document Analysis and Recognition.28–42. (LNCS.)
Abstract: The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets.
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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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