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Joan Mas |
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Title |
Syntactic approaches to recognize bi-dimensional shapes in graphics recognition. Application to sketching interfaces |
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2005 |
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CVC Technical Report #86 |
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CVC (UAB) |
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DAG @ dag @ Mas2005a |
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573 |
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Author |
Jaume Rodriguez; S. Yacoub; Gemma Sanchez; Josep Llados |
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Title |
Performance Evaluation, Comparison and Combination of Commercial Handwriting Recognition Engines |
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2006 |
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CVC Technical Report #93 |
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DAG @ dag @ RYS2006 |
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657 |
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Author |
Marçal Rusiñol |
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Title |
A Model of Vectorial Signatures in Terms of Expressive Sub-Shapes: Symbol Indexation in Technical Documents |
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2006 |
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CVC Technical Report #94 |
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DAG @ dag @ Rus2006 |
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668 |
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Author |
Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli |
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Title |
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts |
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Conference Article |
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2022 |
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Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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13639 |
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3-12 |
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N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections |
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Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ GBS2022 |
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3733 |
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Author |
Arnau Baro; Pau Riba; Alicia Fornes |
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Title |
Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network |
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2022 |
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Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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13639 |
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171-184 |
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Object detection; Optical music recognition; Graph neural network |
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During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.162; 600.140; 602.230 |
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Admin @ si @ BRF2022b |
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3740 |
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Author |
Subhajit Maity; Sanket Biswas; Siladittya Manna; Ayan Banerjee; Josep Llados; Saumik Bhattacharya; Umapada Pal |
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Title |
SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation |
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Conference Article |
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2023 |
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17th International Conference on Doccument Analysis and Recognition |
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14187 |
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342–360 |
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Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: this https URL |
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Document Layout Analysis; Document |
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ICDAR |
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Admin @ si @ MBM2023 |
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3990 |
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Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados |
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Title |
Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval |
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Conference Article |
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2011 |
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33rd European Conference on Information Retrieval |
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6611 |
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314-325 |
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In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset. |
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Dublin, Ireland |
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Springer |
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Berlin |
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P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch |
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978-3-642-20160-8 |
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ECIR |
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DAG; RV;ADAS |
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Admin @ si @ RAK2011 |
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1737 |
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Author |
Jaime Lopez-Krahe; Josep Llados; Enric Marti |
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Architectural Floor Plan Analysis |
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2000 |
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CVonline |
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Edimburg, UK |
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University of Edinburgh |
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Robert B. Fisher |
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online pdf |
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DAG;IAM |
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IAM @ iam @ LLM2000 |
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1561 |
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Agnes Borras; Josep Llados |
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Object Image Retrieval by Shape Content in Complex Scenes Using Geometric Constraints |
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2005 |
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Pattern Recognition And Image Analysis |
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LNCS |
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3522 |
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325–332 |
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This paper presents an image retrieval system based on 2D shape information. Query shape objects and database images are repre- sented by polygonal approximations of their contours. Afterwards they are encoded, using geometric features, in terms of predefined structures. Shapes are then located in database images by a voting procedure on the spatial domain. Then an alignment matching provides a probability value to rank de database image in the retrieval result. The method al- lows to detect a query object in database images even when they contain complex scenes. Also the shape matching tolerates partial occlusions and affine transformations as translation, rotation or scaling. |
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Estoril (Portugal) |
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Springer Link |
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DAG @ dag @ BoL2005; IAM @ iam @ BoL2005 |
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556 |
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Joan Mas; Gemma Sanchez; Josep Llados |
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Title |
An Adjacency Grammar to Recognize Symbols and Gestures in a Digital Pen Framework |
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2005 |
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Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 115–122 |
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DAG @ dag @ MSL2005a |
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558 |
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