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Author |
Arnau Baro; Alicia Fornes; Carles Badal |
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Title |
Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism |
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Conference Article |
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2020 |
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17th International Conference on Frontiers in Handwriting Recognition |
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Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
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Virtual ICFHR; September 2020 |
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ICFHR |
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DAG; 600.140; 600.121 |
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Admin @ si @ BFB2020 |
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3448 |
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Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
Distilling Content from Style for Handwritten Word Recognition |
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Conference Article |
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2020 |
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17th International Conference on Frontiers in Handwriting Recognition |
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Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten
word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes.
Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both
the generative process and the recognition efficiency in the IAM dataset. |
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Virtual ICFHR; September 2020 |
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ICFHR |
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DAG; 600.129; 600.140; 600.121 |
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Admin @ si @ KRR2020a |
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3425 |
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L.Tarazon; D. Perez; N. Serrano; V. Alabau; Oriol Ramos Terrades; A. Sanchis; A. Juan |
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Title |
Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text |
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2009 |
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15th International Conference on Image Analysis and Processing |
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5716 |
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567-574 |
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An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence. |
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Vietri sul Mare, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04145-7 |
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ICIAP |
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DAG |
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Admin @ si @ TPS2009 |
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1871 |
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Author |
Andreas Fischer; Ching Y. Suen; Volkmar Frinken; Kaspar Riesen; Horst Bunke |
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Title |
A Fast Matching Algorithm for Graph-Based Handwriting Recognition |
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2013 |
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9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition |
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7877 |
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194-203 |
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The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy. |
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Vienna; Austria; May 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38220-8 |
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GBR |
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DAG; 600.045; 605.203 |
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Admin @ si @ FSF2013 |
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2294 |
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Author |
Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model |
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Conference Article |
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2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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399-404 |
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Named entity recognition; Handwritten Text Recognition; neural networks |
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When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing. |
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Vienna; Austria; April 2018 |
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DAG; 600.097; 603.057; 601.311; 600.121 |
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Admin @ si @ CVF2018 |
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3170 |
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Author |
Joan Mas; B. Lamiroy; Gemma Sanchez; Josep Llados |
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Title |
Automatic Learning of Symbol Descriptions Avoiding Topological Ambiguities |
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Miscellaneous |
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2006 |
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3rd Eurographics Workshop on Sketch Based Interfaces and Modeling (SBIM´06), 27–34 |
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Vienna (Austria) |
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DAG @ dag @ MLS2006b |
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710 |
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Jose Antonio Rodriguez; Gemma Sanchez; Josep Llados |
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Title |
Automatic Interpretation of Proofreading Sketches |
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2006 |
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3rd Eurographics Workshop on Sketch Based Interfaces and Modeling (SBIM´06), 35–42 |
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Vienna (Austria) |
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DAG |
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DAG @ dag @ RSL2006a |
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716 |
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Author |
Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters |
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Conference Article |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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97-102 |
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Robust Reading; End-to-end Systems; CNN; Utility Meters |
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In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur. |
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Viena; Austria; April 2018 |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GRK2018 |
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3102 |
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Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou |
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Title |
The Robust Reading Competition Annotation and Evaluation Platform |
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Conference Article |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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61-66 |
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The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the
Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services. |
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Viena; Austria; April 2018 |
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DAG; 600.084; 600.121 |
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KGR2018 |
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3103 |
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David Aldavert; Marçal Rusiñol |
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Title |
Manuscript text line detection and segmentation using second-order derivatives analysis |
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Conference Article |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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293 - 298 |
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text line detection; text line segmentation; text region detection; second-order derivatives |
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In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets. |
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Viena; Austria; April 2018 |
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DAG; 600.084; 600.129; 302.065; 600.121 |
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Admin @ si @ AlR2018a |
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3104 |
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