Adria Molina, Pau Riba, Lluis Gomez, Oriol Ramos Terrades, & Josep Llados. (2021). Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach. In 16th International Conference on Document Analysis and Recognition (Vol. 12822, pp. 306–320). LNCS.
Abstract: This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.
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Albert Suso, Pau Riba, Oriol Ramos Terrades, & Josep Llados. (2021). A Self-supervised Inverse Graphics Approach for Sketch Parametrization. In 16th International Conference on Document Analysis and Recognition (Vol. 12916, pp. 28–42). LNCS.
Abstract: The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets.
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Pau Riba, Adria Molina, Lluis Gomez, Oriol Ramos Terrades, & Josep Llados. (2021). Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting. In 16th International Conference on Document Analysis and Recognition (Vol. 12822, 381–395).
Abstract: In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
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Ruben Tito, Dimosthenis Karatzas, & Ernest Valveny. (2021). Document Collection Visual Question Answering. In 16th International Conference on Document Analysis and Recognition (Vol. 12822, pp. 778–792). LNCS.
Abstract: Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task.
Keywords: Document collection; Visual Question Answering
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Ruben Tito, Minesh Mathew, C.V. Jawahar, Ernest Valveny, & Dimosthenis Karatzas. (2021). ICDAR 2021 Competition on Document Visual Question Answering. In 16th International Conference on Document Analysis and Recognition (pp. 635–649).
Abstract: In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5, 000 infographics images and 30, 000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented.
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Sanket Biswas, Pau Riba, Josep Llados, & Umapada Pal. (2021). DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis. In 16th International Conference on Document Analysis and Recognition (Vol. 12823, 555–568). LNCS.
Abstract: Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind.
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Sanket Biswas, Pau Riba, Josep Llados, & Umapada Pal. (2021). Graph-Based Deep Generative Modelling for Document Layout Generation. In 16th International Conference on Document Analysis and Recognition (Vol. 12917, pp. 525–537). LNCS.
Abstract: One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.
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