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Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal |
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Title |
DocEnTr: An End-to-End Document Image Enhancement Transformer |
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Conference Article |
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Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
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1699-1705 |
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Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads |
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Abstract |
Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR |
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August 21-25, 2022 , Montréal Québec |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ SBJ2022 |
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3730 |
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Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Word Spotting in Scene Images based on Character Recognition |
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Conference Article |
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Year |
2018 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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1872-1874 |
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In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images. |
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Salt Lake City; USA; June 2018 |
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CVPRW |
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DAG; 600.129; 600.121 |
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BKB2018a |
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3179 |
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Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Large-scale document image retrieval and classification with runlength histograms and binary embeddings |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
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PR |
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46 |
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7 |
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1898-1905 |
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Keywords |
visual document descriptor; compression; large-scale; retrieval; classification |
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We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ GPV2013 |
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2306 |
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Author |
Albert Gordo; Florent Perronnin |
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Title |
A Bag-of-Pages Approach to Unordered Multi-Page Document Classification |
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2010 |
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20th International Conference on Pattern Recognition |
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Pages |
1920–1923 |
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We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly outperforms a baseline system. |
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Istanbul (Turkey) |
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1051-4651 |
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978-1-4244-7542-1 |
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DAG |
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Admin @ si @ GoP2010 |
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1480 |
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Author |
Khanh Nguyen; Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Show, Interpret and Tell: Entity-Aware Contextualised Image Captioning in Wikipedia |
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Conference Article |
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2023 |
Publication |
Proceedings of the 37th AAAI Conference on Artificial Intelligence |
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37 |
Issue |
2 |
Pages |
1940-1948 |
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Humans exploit prior knowledge to describe images, and are able to adapt their explanation to specific contextual information given, even to the extent of inventing plausible explanations when contextual information and images do not match. In this work, we propose the novel task of captioning Wikipedia images by integrating contextual knowledge. Specifically, we produce models that jointly reason over Wikipedia articles, Wikimedia images and their associated descriptions to produce contextualized captions. The same Wikimedia image can be used to illustrate different articles, and the produced caption needs to be adapted to the specific context allowing us to explore the limits of the model to adjust captions to different contextual information. Dealing with out-of-dictionary words and Named Entities is a challenging task in this domain. To address this, we propose a pre-training objective, Masked Named Entity Modeling (MNEM), and show that this pretext task results to significantly improved models. Furthermore, we verify that a model pre-trained in Wikipedia generalizes well to News Captioning datasets. We further define two different test splits according to the difficulty of the captioning task. We offer insights on the role and the importance of each modality and highlight the limitations of our model. |
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Washington; USA; February 2023 |
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AAAI |
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DAG |
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Admin @ si @ NBM2023 |
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3860 |
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Author |
Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi |
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Title |
WiCV 2018: The Fourth Women In Computer Vision Workshop |
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Conference Article |
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2018 |
Publication |
4th Women in Computer Vision Workshop |
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1941-19412 |
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Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning |
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Abstract |
We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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Salt Lake City; USA; June 2018 |
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WiCV |
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DAG; 600.121; 600.129 |
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Call Number |
Admin @ si @ DBR2018 |
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3222 |
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Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Ernest Valveny |
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Title |
Symbol Classification using Dynamic Aligned Shape Descriptor |
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Conference Article |
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2010 |
Publication |
20th International Conference on Pattern Recognition |
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1957–1960 |
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Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates. |
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Istanbul (Turkey) |
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1051-4651 |
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978-1-4244-7542-1 |
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DAG; HUPBA; MILAB |
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BCNPCL @ bcnpcl @ FEL2010 |
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1421 |
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Author |
Anjan Dutta; Umapada Pal; Alicia Fornes; Josep Llados |
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Title |
An Efficient Staff Removal Technique from Printed Musical Documents |
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Conference Article |
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2010 |
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20th International Conference on Pattern Recognition |
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1965–1968 |
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Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have considered a staffline segment as a horizontal linkage of vertical black runs with uniform height. We have used the neighbouring properties of a staffline segment to validate it as a true segment. We have considered the dataset along with the deformations described in for evaluation purpose. From experimentation we have got encouraging results. |
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Istanbul (Turkey) |
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1051-4651 |
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978-1-4244-7542-1 |
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DAG @ dag @ DPF2010 |
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1420 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados; Mathieu Nicolas Delalandre |
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Title |
Multi-oriented touching text character segmentation in graphical documents using dynamic programming |
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Journal Article |
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Year |
2012 |
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Pattern Recognition |
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PR |
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45 |
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5 |
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1972-1983 |
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2,292 JCR
The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes. |
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Admin @ si @ RPL2012a |
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2133 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Feature Selection on Node Statistics Based Embedding of Graphs |
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2012 |
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Pattern Recognition Letters |
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PRL |
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33 |
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15 |
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1980–1990 |
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Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification |
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Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods. |
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Admin @ si @ GVB2012b |
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1993 |
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