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David Aldavert, Marçal Rusiñol, Ricardo Toledo and Josep Llados. 2015. A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting. IJDAR, 18(3), 223–234.
Abstract: The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014.
Keywords: Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation
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Pau Riba, Josep Llados and Alicia Fornes. 2015. Handwritten Word Spotting by Inexact Matching of Grapheme Graphs. 13th International Conference on Document Analysis and Recognition ICDAR2015.781–785.
Abstract: This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections.
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Juan Ignacio Toledo, Jordi Cucurull, Jordi Puiggali, Alicia Fornes and Josep Llados. 2015. Document Analysis Techniques for Automatic Electoral Document Processing: A Survey. E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015.139–141. (LNCS.)
Abstract: In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents.
Keywords: Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally
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Nuria Cirera, Alicia Fornes and Josep Llados. 2015. Hidden Markov model topology optimization for handwriting recognition. 13th International Conference on Document Analysis and Recognition ICDAR2015.626–630.
Abstract: In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task.
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Youssef El Rhabi, Simon Loic and Brun Luc. 2015. Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel. 15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015.
Abstract: Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data.
Keywords: Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration
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Pau Riba, Josep Llados, Alicia Fornes and Anjan Dutta. 2015. Large-scale Graph Indexing using Binary Embeddings of Node Contexts. In C.-L.Liu, B.Luo, W.G.Kropatsch and J.Cheng, eds. 10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition. Springer International Publishing, 208–217. (LNCS.)
Abstract: Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.
Keywords: Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding
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Olivier Lefebvre and 6 others. 2015. Monitoring neuromotricity on-line: a cloud computing approach. 17th Conference of the International Graphonomics Society IGS2015.
Abstract: The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain.
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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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Christophe Rigaud, Clement Guerin, Dimosthenis Karatzas, Jean-Christophe Burie and Jean-Marc Ogier. 2015. Knowledge-driven understanding of images in comic books. IJDAR, 18(3), 199–221.
Abstract: Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way.
Keywords: Document Understanding; comics analysis; expert system
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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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