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Salvatore Tabbone, Oriol Ramos Terrades and S. Barrat. 2008. Histogram of radon transform. A useful descriptor for shape retrieval. 19th International Conference on Pattern Recognition.1–4.
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Lluis Gomez, Dena Bazazian and Dimosthenis Karatzas. 2020. Historical review of scene text detection research. 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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Joan Mas, Jose Antonio Rodriguez, Dimosthenis Karatzas, Gemma Sanchez and Josep Llados. 2008. HistoSketch: A Semi-Automatic Annotation Tool for Archival Documents. Proceedings of the 8th International Workshop on Document Analysis Systems,.517–524.
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Dimosthenis Karatzas, V. Poulain d'Andecy and Marçal Rusiñol. 2016. Human-Document Interaction – a new frontier for document image analysis. 12th IAPR Workshop on Document Analysis Systems.369–374.
Abstract: All indications show that paper documents will not cede in favour of their digital counterparts, but will instead be used increasingly in conjunction with digital information. An open challenge is how to seamlessly link the physical with the digital – how to continue taking advantage of the important affordances of paper, without missing out on digital functionality. This paper
presents the authors’ experience with developing systems for Human-Document Interaction based on augmented document interfaces and examines new challenges and opportunities arising for the document image analysis field in this area. The system presented combines state of the art camera-based document
image analysis techniques with a range of complementary tech-nologies to offer fluid Human-Document Interaction. Both fixed and nomadic setups are discussed that have gone through user testing in real-life environments, and use cases are presented that span the spectrum from business to educational application
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Nuria Cirera, Alicia Fornes, Volkmar Frinken and Josep Llados. 2013. Hybrid grammar language model for handwritten historical documents recognition. 6th Iberian Conference on Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 117–124. (LNCS.)
Abstract: In this paper we present a hybrid language model for the recognition of handwritten historical documents with a structured syntactical layout. Using a hidden Markov model-based recognition framework, a word-based grammar with a closed dictionary is enhanced by a character sequence recognition method. This allows to recognize out-of-dictionary words in controlled parts of the recognition, while keeping a closed vocabulary restriction for other parts. While the current status is work in progress, we can report an improvement in terms of character error rate.
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Asma Bensalah, Antonio Parziale, Giuseppe De Gregorio, Angelo Marcelli, Alicia Fornes and Josep Llados. 2023. I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation. 21st International Graphonomics Conference.136–148.
Abstract: During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation.
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Josep Llados, Felipe Lumbreras, V. Chapaprieta and J. Queralt. 2001. ICAR: Identity Card Automatic Reader..
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Dimosthenis Karatzas, Sergi Robles, Joan Mas, Farshad Nourbakhsh and Partha Pratim Roy. 2011. ICDAR 2011 Robust Reading Competition – Challege 1: Reading Text in Born-Digital Images (Web and Email). 11th International Conference on Document Analysis and Recognition.1485–1490.
Abstract: This paper presents the results of the first Challenge of ICDAR 2011 Robust Reading Competition. Challenge 1 is focused on the extraction of text from born-digital images, specifically from images found in Web pages and emails. The challenge was organized in terms of three tasks that look at different stages of the process: text localization, text segmentation and word recognition. In this paper we present the results of the challenge for all three tasks, and make an open call for continuous participation outside the context of ICDAR 2011.
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Dimosthenis Karatzas and 9 others. 2013. ICDAR 2013 Robust Reading Competition. 12th International Conference on Document Analysis and Recognition.1484–1493.
Abstract: This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.
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Dimosthenis Karatzas and 12 others. 2015. ICDAR 2015 Competition on Robust Reading. 13th International Conference on Document Analysis and Recognition ICDAR2015.1156–1160.
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