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Ernest Valveny and Enric Marti. 2000. Hand-drawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework. Proc. 15th Int Pattern Recognition Conf.239–242.
Abstract: Hand-drawn symbols can take many different and distorted shapes from their ideal representation. Then, very flexible methods are needed to be able to handle unconstrained drawings. We propose here to extend our previous work in hand-drawn symbol recognition based on a Bayesian framework and deformable template matching. This approach gets flexibility enough to fit distorted shapes in the drawing while keeping fidelity to the ideal shape of the symbol. In this work, we define the similarity measure between an image and a symbol based on the distance from every pixel in the image to the lines in the symbol. Matching is carried out using an implementation of the EM algorithm. Thus, we can improve recognition rates and computation time with respect to our previous formulation based on a simulated annealing algorithm.
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G.Thorvaldsen and 6 others. 2015. A Tale of two Transcriptions.
Abstract: non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources.
Keywords: Nominative Sources; Census; Vital Records; Computer Vision; Optical Character Recognition; Word Spotting
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Robert Benavente, Gemma Sanchez, Ramon Baldrich, Maria Vanrell and Josep Llados. 2000. Normalized colour segmentation for human appearance description. 15 th International Conference on Pattern Recognition.637–641.
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Josep Llados, Jaime Lopez-Krahe, Gemma Sanchez and Enric Marti. 2000. Interprétation de cartes et plans par mise en correspondance de graphes de attributs. 12 Congrès Francophone AFRIF–AFIA.225–234.
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Carles Sanchez, Oriol Ramos Terrades, Patricia Marquez, Enric Marti, J.Roncaries and Debora Gil. 2015. Automatic evaluation of practices in Moodle for Self Learning in Engineering.
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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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Marçal Rusiñol, R.Roset, Josep Llados and C.Montaner. 2011. Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation.
Abstract: By means of computer vision algorithms scanned images of maps are processed in order to extract relevant geographic information from printed coordinate pairs. The meaningful information is then transformed into georeferencing information for each single map sheet, and the complete set is compiled to produce a graphical index sheet for the map series along with relevant metadata. The whole process is fully automated and trained to attain maximum effectivity and throughput.
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Antonio Clavelli, Dimosthenis Karatzas, Josep Llados, Mario Ferraro and Giuseppe Boccignone. 2014. Modelling task-dependent eye guidance to objects in pictures. CoCom, 6(3), 558–584.
Abstract: 5Y Impact Factor: 1.14 / 3rd (Computer Science, Artificial Intelligence)
We introduce a model of attentional eye guidance based on the rationale that the deployment of gaze is to be considered in the context of a general action-perception loop relying on two strictly intertwined processes: sensory processing, depending on current gaze position, identifies sources of information that are most valuable under the given task; motor processing links such information with the oculomotor act by sampling the next gaze position and thus performing the gaze shift. In such a framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the payoff of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. The different levels of the action-perception loop are represented in probabilistic form and eventually give rise to a stochastic process that generates the gaze sequence. This way the model also accounts for statistical properties of gaze shifts such as individual scan path variability. Results of the simulations are compared either with experimental data derived from publicly available datasets and from our own experiments.
Keywords: Visual attention; Gaze guidance; Value; Payoff; Stochastic fixation prediction
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L. Rothacker, Marçal Rusiñol, Josep Llados and G.A. Fink. 2014. A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting.
Abstract: With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline.
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Josep Llados and Enric Marti. 1999. A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems. Machine Graphics & Vision, 8, 195–211.
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