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Minesh Mathew; Ruben Tito; Dimosthenis Karatzas; R.Manmatha; C.V. Jawahar |
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Document Visual Question Answering Challenge 2020 |
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2020 |
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33rd IEEE Conference on Computer Vision and Pattern Recognition – Short paper |
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This paper presents results of Document Visual Question Answering Challenge organized as part of “Text and Documents in the Deep Learning Era” workshop, in CVPR 2020. The challenge introduces a new problem – Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer(s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template. |
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DAG; 600.121 |
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Admin @ si @ MTK2020 |
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3558 |
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Lasse Martensson; Ekta Vats; Anders Hast; Alicia Fornes |
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Title |
In Search of the Scribe: Letter Spotting as a Tool for Identifying Scribes in Large Handwritten Text Corpora |
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2019 |
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Journal for Information Technology Studies as a Human Science |
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HUMAN IT |
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14 |
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2 |
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95-120 |
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Scribal attribution/ writer identification; digital palaeography; word spotting; mediaeval charters; mediaeval manuscripts |
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In this article, a form of the so-called word spotting-method is used on a large set of handwritten documents in order to identify those that contain script of similar execution. The point of departure for the investigation is the mediaeval Swedish manuscript Cod. Holm. D 3. The main scribe of this manuscript has yet not been identified in other documents. The current attempt aims at localising other documents that display a large degree of similarity in the characteristics of the script, these being possible candidates for being executed by the same hand. For this purpose, the method of word spotting has been employed, focusing on individual letters, and therefore the process is referred to as letter spotting in the article. In this process, a set of ‘g’:s, ‘h’:s and ‘k’:s have been selected as templates, and then a search has been made for close matches among the mediaeval Swedish charters. The search resulted in a number of charters that displayed great similarities with the manuscript D 3. The used letter spotting method thus proofed to be a very efficient sorting tool localising similar script samples. |
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DAG; 600.097; 600.140; 600.121 |
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Admin @ si @ MVH2019 |
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3234 |
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Helena Muñoz; Fernando Vilariño; Dimosthenis Karatzas |
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Title |
Eye-Movements During Information Extraction from Administrative Documents |
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2019 |
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International Conference on Document Analysis and Recognition Workshops |
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6-9 |
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A key aspect of digital mailroom processes is the extraction of relevant information from administrative documents. More often than not, the extraction process cannot be fully automated, and there is instead an important amount of manual intervention. In this work we study the human process of information extraction from invoice document images. We explore whether the gaze of human annotators during an manual information extraction process could be exploited towards reducing the manual effort and automating the process. To this end, we perform an eye-tracking experiment replicating real-life interfaces for information extraction. Through this pilot study we demonstrate that relevant areas in the document can be identified reliably through automatic fixation classification, and the obtained models generalize well to new subjects. Our findings indicate that it is in principle possible to integrate the human in the document image analysis loop, making use of the scanpath to automate the extraction process or verify extracted information. |
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Sydney; Australia; September 2019 |
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ICDARW |
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DAG; 600.140; 600.121; 600.129;SIAI |
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Admin @ si @ MVK2019 |
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3336 |
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Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño |
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Title |
Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos |
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2014 |
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CARE workshop |
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Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection |
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We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels. |
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Boston; USA; September 2014 |
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CARE |
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MV; DAG; 600.060; 600.047; 600.077;SIAI |
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Admin @ si @ NBF2014 |
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2504 |
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A.Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas |
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Title |
Sparse Radial Sampling LBP for Writer Identification |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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716-720 |
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In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077 |
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Admin @ si @ NBL2015 |
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2692 |
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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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2023 |
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Proceedings of the 37th AAAI Conference on Artificial Intelligence |
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37 |
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2 |
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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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Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas |
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Title |
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings |
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2018 |
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International Workshop on Reproducible Research in Pattern Recognition |
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11455 |
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71-82 |
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Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. |
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DAG; 600.121; 600.129 |
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Admin @ si @ NDC2018 |
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3178 |
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Francesc Net; Marc Folia; Pep Casals; Lluis Gomez |
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Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14191 |
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3-17 |
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Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning |
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This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset. |
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San Jose; CA; USA; August 2023 |
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DAG |
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Admin @ si @ NFC2023 |
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3859 |
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Author |
Farshad Nourbakhsh |
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Colour logo recognition |
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2009 |
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CVC Technical Report |
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145 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Nou2009 |
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2399 |
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Nibal Nayef; Yash Patel; Michal Busta; Pinaki Nath Chowdhury; Dimosthenis Karatzas; Wafa Khlif; Jiri Matas; Umapada Pal; Jean-Christophe Burie; Cheng-lin Liu; Jean-Marc Ogier |
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ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019 |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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1582-1587 |
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With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.121; 600.129 |
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Admin @ si @ NPB2019 |
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3341 |
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