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Lluis Pere de las Heras, Ahmed Sheraz, Marcus Liwicki, Ernest Valveny and Gemma Sanchez. 2014. Statistical Segmentation and Structural Recognition for Floor Plan Interpretation. IJDAR, 17(3), 221–237.
Abstract: A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.
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Anders Hast and Alicia Fornes. 2016. A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching. 12th IAPR Workshop on Document Analysis Systems.150–155.
Abstract: The automatic recognition of historical handwritten documents is still considered challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results.
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Lluis Gomez, Anguelos Nicolaou, Marçal Rusiñol and Dimosthenis Karatzas. 2020. 12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding. In K. Alahari and C.V. Jawahar, eds. Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis. Springer. (Series on Advances in Computer Vision and Pattern Recognition.)
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Raul Gomez, Ali Furkan Biten, Lluis Gomez, Jaume Gibert, Marçal Rusiñol and Dimosthenis Karatzas. 2019. Selective Style Transfer for Text. 15th International Conference on Document Analysis and Recognition.805–812.
Abstract: This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available.
Keywords: transfer; text style transfer; data augmentation; scene text detection
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Alicia Fornes, Josep Llados, Gemma Sanchez, Xavier Otazu and Horst Bunke. 2010. A Combination of Features for Symbol-Independent Writer Identification in Old Music Scores. IJDAR, 13(4), 243–259.
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 an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers.
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N. Zakaria, Jean-Marc Ogier and Josep Llados. 2006. The Fuzzy-Spatial Descriptor for the Online Graphic Recognition: Overlapping Matrix Algorithm. 7th International Workshop, Document Analysis Systems VII (DAS´06), LNCS 3872: 616–627.
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N. Zakaria, Jean-Marc Ogier and Josep Llados. 2005. On-line Graphics Recognition based on Invariant Spatio-Sequential Descriptor: Fuzzy Matrix.
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Liu Wenyin, Josep Llados and Jean-Marc Ogier. 2008. Graphics Recognition. Recent Advances and New Opportunities.. (LNCS.)
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Ernest Valveny, Salvatore Tabbone and Oriol Ramos Terrades. 2008. Performance Characterization of Shape Descriptors for Symbol Representation. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.278–287. (LNCS.)
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Ernest Valveny, Salvatore Tabbone, Oriol Ramos Terrades and Emilie Jean-Marie Odile. 2007. Performance Characterization of Shape Descriptors for Symbol Representation. Seventh IAPR International Workshop on Graphics Recognition.82–83.
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