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Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
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Title |
Hierarchical multimodal transformers for Multi-Page DocVQA |
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2023 |
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Pattern Recognition |
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PR |
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144 |
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109834 |
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Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that should be processed altogether. In this work we extend DocVQA to the multi-page scenario. For that, we first create a new dataset, MP-DocVQA, where questions are posed over multi-page documents instead of single pages. Second, we propose a new hierarchical method, Hi-VT5, based on the T5 architecture, that overcomes the limitations of current methods to process long multi-page documents. The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer. Through extensive experimentation, we demonstrate that our method is able, in a single stage, to answer the questions and provide the page that contains the relevant information to find the answer, which can be used as a kind of explainability measure. |
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ISSN 0031-3203 |
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DAG; 600.155; 600.121 |
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Admin @ si @ TKV2023 |
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3825 |
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Author |
Sergi Garcia Bordils; George Tom; Sangeeth Reddy; Minesh Mathew; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas |
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Title |
Read While You Drive-Multilingual Text Tracking on the Road |
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Conference Article |
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2022 |
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15th IAPR International workshop on document analysis systems |
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13237 |
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756–770 |
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Visual data obtained during driving scenarios usually contain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully annotated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods exploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild. |
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La Rochelle; France; May 2022 |
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978-3-031-06554-5 |
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DAG; 600.155; 611.022; 611.004 |
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Admin @ si @ GTR2022 |
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3783 |
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Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados |
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Title |
Table detection in business document images by message passing networks |
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Journal Article |
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Year |
2022 |
Publication |
Pattern Recognition |
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PR |
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127 |
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108641 |
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Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. |
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July 2022 |
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Elsevier |
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DAG; 600.162; 600.121 |
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Admin @ si @ RGR2022 |
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3729 |
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Author |
Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai |
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Title |
Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks |
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Conference Article |
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2022 |
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17th European Conference on Computer Vision Workshops |
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13804 |
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329–344 |
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Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection. |
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978-3-031-25068-2 |
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ECCV-TiE |
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DAG; 600.162; 600.140; 110.312 |
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Admin @ si @ GBC2022 |
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3795 |
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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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Conference Article |
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Year |
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 |
Marçal Rusiñol; T.Benkhelfallah; V. Poulain d'Andecy |
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Title |
Field Extraction from Administrative Documents by Incremental Structural Templates |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1100 - 1104 |
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In this paper we present an incremental framework aimed at extracting field information from administrative document images in the context of a Digital Mail-room scenario. Given a single training sample in which the user has marked which fields have to be extracted from a particular document class, a document model representing structural relationships among words is built. This model is incrementally refined as the system processes more and more documents from the same class. A reformulation of the tf-idf statistic scheme allows to adjust the importance weights of the structural relationships among words. We report in the experimental section our results obtained with a large dataset of real invoices. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.56; 600.045; 605.203; 602.101 |
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Admin @ si @ RBP2013 |
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2346 |
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Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi |
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Title |
Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars |
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Journal Article |
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2015 |
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Neurocomputing |
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NEUCOM |
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150 |
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A |
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147-154 |
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document image analysis; stochastic context-free grammars; text classi cation features |
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In this paper we de ne a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classi cation features are used to perform an initial classi cation of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models
and the results showed that the proposed grammatical model outperformed
the other methods. Furthermore, grammars also provide the document structure
along with its segmentation. |
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DAG; 601.158; 600.077; 600.061 |
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Admin @ si @ ACS2015 |
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2531 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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A fine-grained approach to scene text script identification |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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192-197 |
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This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online. |
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Santorini; Grecia; April 2016 |
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DAG; 601.197; 600.084 |
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Admin @ si @ GoK2016b |
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2863 |
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Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
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Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images |
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2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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181 - 185 |
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Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. |
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Tours; France; April 2014 |
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978-1-4799-3243-6 |
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DAG; 601.223; 600.077 |
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Admin @ si @ RCO2014a |
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2545 |
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Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
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Normalisation et validation d'images de documents capturées en mobilité |
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2014 |
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Colloque International Francophone sur l'Écrit et le Document |
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109-124 |
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mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction |
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Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods. |
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Nancy; France; March 2014 |
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CIFED |
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DAG; 601.223; 600.077 |
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Admin @ si @ RCO2014b |
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2546 |
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