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Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
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Title |
Beyond Document Object Detection: Instance-Level Segmentation of Complex Layouts |
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2021 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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24 |
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269–281 |
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Information extraction is a fundamental task of many business intelligence services that entail massive document processing. Understanding a document page structure in terms of its layout provides contextual support which is helpful in the semantic interpretation of the document terms. In this paper, inspired by the progress of deep learning methodologies applied to the task of object recognition, we transfer these models to the specific case of document object detection, reformulating the traditional problem of document layout analysis. Moreover, we importantly contribute to prior arts by defining the task of instance segmentation on the document image domain. An instance segmentation paradigm is especially important in complex layouts whose contents should interact for the proper rendering of the page, i.e., the proper text wrapping around an image. Finally, we provide an extensive evaluation, both qualitative and quantitative, that demonstrates the superior performance of the proposed methodology over the current state of the art. |
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DAG; 600.121; 600.140; 110.312 |
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Admin @ si @ BRL2021b |
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3574 |
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Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
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Title |
DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis |
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Conference Article |
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2021 |
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16th International Conference on Document Analysis and Recognition |
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12823 |
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555–568 |
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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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Lausanne; Suissa; September 2021 |
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DAG; 600.121; 600.140; 110.312 |
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Admin @ si @ BRL2021a |
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3573 |
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Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
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Title |
Graph-Based Deep Generative Modelling for Document Layout Generation |
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Conference Article |
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2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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12917 |
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525-537 |
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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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Lausanne; Suissa; September 2021 |
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DAG; 600.121; 600.140; 110.312 |
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Admin @ si @ BRL2021 |
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3676 |
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