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Author |
Adarsh Tiwari; Sanket Biswas; Josep Llados |
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Title |
Can Pre-trained Language Models Help in Understanding Handwritten Symbols? |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
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14193 |
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199–211 |
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Abstract  |
The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ TBL2023 |
Serial |
3908 |
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Author |
Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
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Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
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Conference Article |
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Year |
2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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10029 |
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543-552 |
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Keywords |
Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
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Abstract |
The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
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Merida; Mexico; December 2016 |
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Springer International Publishing |
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978-3-319-49054-0 |
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S+SSPR |
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DAG; 600.097; 602.006 |
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no |
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Admin @ si @ TSF2016 |
Serial |
2877 |
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Author |
Antonio Clavelli; Dimosthenis Karatzas |
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Title |
Text Segmentation in Colour Posters from the Spanish Civil War Era |
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Conference Article |
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Year |
2009 |
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10th International Conference on Document Analysis and Recognition |
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181 - 185 |
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The extraction of textual content from colour documents of a graphical nature is a complicated task. The text can be rendered in any colour, size and orientation while the existence of complex background graphics with repetitive patterns can make its localization and segmentation extremely difficult.
Here, we propose a new method for extracting textual content from such colour images that makes no assumption as to the size of the characters, their orientation or colour, while it is tolerant to characters that do not follow a straight baseline. We evaluate this method on a collection of documents with historical
connotations: the Posters from the Spanish Civil War. |
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Barcelona, Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG |
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no |
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DAG @ dag @ ClK2009 |
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1172 |
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Author |
Souhail Bakkali; Sanket Biswas; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades; Josep Llados |
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Title |
TransferDoc: A Self-Supervised Transferable Document Representation Learning Model Unifying Vision and Language |
Type |
Miscellaneous |
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Year |
2023 |
Publication |
Arxiv |
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The field of visual document understanding has witnessed a rapid growth in emerging challenges and powerful multi-modal strategies. However, they rely on an extensive amount of document data to learn their pretext objectives in a ``pre-train-then-fine-tune'' paradigm and thus, suffer a significant performance drop in real-world online industrial settings. One major reason is the over-reliance on OCR engines to extract local positional information within a document page. Therefore, this hinders the model's generalizability, flexibility and robustness due to the lack of capturing global information within a document image. We introduce TransferDoc, a cross-modal transformer-based architecture pre-trained in a self-supervised fashion using three novel pretext objectives. TransferDoc learns richer semantic concepts by unifying language and visual representations, which enables the production of more transferable models. Besides, two novel downstream tasks have been introduced for a ``closer-to-real'' industrial evaluation scenario where TransferDoc outperforms other state-of-the-art approaches. |
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DAG |
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no |
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Call Number |
Admin @ si @ BBM2023 |
Serial |
3995 |
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Author |
Alicia Fornes; V.C.Kieu; M. Visani; N.Journet; Anjan Dutta |
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Title |
The ICDAR/GREC 2013 Music Scores Competition: Staff Removal |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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Volume |
8746 |
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Pages |
207-220 |
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Keywords |
Competition; Graphics recognition; Music scores; Writer identification; Staff removal |
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Abstract |
The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations. |
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Springer Berlin Heidelberg |
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B.Lamiroy; J.-M. Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; 600.077; 600.061 |
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no |
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Admin @ si @ FKV2014 |
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2581 |
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Author |
M. Visani; V.C.Kieu; Alicia Fornes; N.Journet |
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Title |
The ICDAR 2013 Music Scores Competition: Staff Removal |
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Conference Article |
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2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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1439-1443 |
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The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.045; 600.061 |
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no |
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Admin @ si @ VKF2013 |
Serial |
2338 |
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Author |
Olivier Lefebvre; Pau Riba; Charles Fournier; Alicia Fornes; Josep Llados; Rejean Plamondon; Jules Gagnon-Marchand |
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Title |
Monitoring neuromotricity on-line: a cloud computing approach |
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Conference Article |
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2015 |
Publication |
17th Conference of the International Graphonomics Society IGS2015 |
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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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Pointe-à-Pitre; Guadeloupe; June 2015 |
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IGS |
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DAG; 600.077 |
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no |
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Admin @ si @ LRF2015 |
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2617 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Vocabulary Selection for Graph of Words Embedding |
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Conference Article |
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2011 |
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5th Iberian Conference on Pattern Recognition and Image Analysis |
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6669 |
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216-223 |
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The Graph of Words Embedding consists in mapping every graph in a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. It has been shown to perform well for graphs with discrete label alphabets. In this paper we extend the methodology to graphs with n-dimensional continuous attributes by selecting node representatives. We propose three different discretization procedures for the attribute space and experimentally evaluate the dependence on both the selector and the number of node representatives. In the context of graph classification, the experimental results reveal that on two out of three public databases the proposed extension achieves superior performance over a standard reference system. |
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Las Palmas de Gran Canaria. Spain |
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Springer |
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Berlin |
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Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario |
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978-3-642-21256-7 |
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IbPRIA |
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DAG |
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no |
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Admin @ si @ GVB2011b |
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1744 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Dimensionality Reduction for Graph of Words Embedding |
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Conference Article |
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2011 |
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8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition |
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6658 |
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22-31 |
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The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs. |
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Münster, Germany |
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Xiaoyi Jiang; Miquel Ferrer; Andrea Torsello |
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978-3-642-20843-0 |
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GbRPR |
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DAG |
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no |
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Admin @ si @ GVB2011a |
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1743 |
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Author |
Zheng Huang; Kai Chen; Jianhua He; Xiang Bai; Dimosthenis Karatzas; Shijian Lu; CV Jawahar |
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Title |
ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction |
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Conference Article |
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Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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1516-1520 |
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The ICDAR 2019 Challenge on “Scanned receipts OCR and key information extraction” (SROIE) covers important aspects related to the automated analysis of scanned receipts. The SROIE tasks play a key role in many document analysis systems and hold significant commercial potential. Although a lot of work has been published over the years on administrative document analysis, the community has advanced relatively slowly, as most datasets have been kept private. One of the key contributions of SROIE to the document analysis community is to offer a first, standardized dataset of 1000 whole scanned receipt images and annotations, as well as an evaluation procedure for such tasks. The Challenge is structured around three tasks, namely Scanned Receipt Text Localization (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). The competition opened on 10th February, 2019 and closed on 5th May, 2019. We received 29, 24 and 18 valid submissions received for the three competition tasks, respectively. This report presents the competition datasets, define the tasks and the evaluation protocols, offer detailed submission statistics, as well as an analysis of the submitted performance. While the tasks of text localization and recognition seem to be relatively easy to tackle, it is interesting to observe the variety of ideas and approaches proposed for the information extraction task. According to the submissions' performance we believe there is still margin for improving information extraction performance, although the current dataset would have to grow substantially in following editions. Given the success of the SROIE competition evidenced by the wide interest generated and the healthy number of submissions from academic, research institutes and industry over different countries, we consider that the SROIE competition can evolve into a useful resource for the community, drawing further attention and promoting research and development efforts in this field. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129 |
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Admin @ si @ HCH2019 |
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3338 |
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