Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer

• URL: http://arxiv.org/abs/2102.09550v1
• Date: Thu, 18 Feb 2021 18:51:47 GMT
• Title: Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
• Authors: Rafa{\l} Powalski, {\L}ukasz Borchmann, Dawid Jurkiewicz, Tomasz Dwojak, Micha{\l} Pietruszka, Gabriela Pa{\l}ka
• Abstract summary: We introduce the TILT neural network architecture which simultaneously learns layout information, visual features, and textual semantics. We trained our network on real-world documents with different layouts, such as tables, figures, and forms.
• Score: 0.6702423358056857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the challenging problem of Natural Language Comprehension beyond plain-text documents by introducing the TILT neural network architecture which simultaneously learns layout information, visual features, and textual semantics. Contrary to previous approaches, we rely on a decoder capable of solving all problems involving natural language. The layout is represented as an attention bias and complemented with contextualized visual information, while the core of our model is a pretrained encoder-decoder Transformer. We trained our network on real-world documents with different layouts, such as tables, figures, and forms. Our novel approach achieves state-of-the-art in extracting information from documents and answering questions, demanding layout understanding (DocVQA, CORD, WikiOps, SROIE). At the same time, we simplify the process by employing an end-to-end model.

Related papers

• See then Tell: Enhancing Key Information Extraction with Vision Grounding [54.061203106565706]
  We introduce STNet (See then Tell Net), a novel end-to-end model designed to deliver precise answers with relevant vision grounding. To enhance the model's seeing capabilities, we collect extensive structured table recognition datasets.
  arXiv  Detail & Related papers  (2024-09-29T06:21:05Z)
• VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning [66.23296689828152]
  We leverage the capabilities of Vision-and-Large-Language Models to enhance in-context emotion classification. In the first stage, we propose prompting VLLMs to generate descriptions in natural language of the subject's apparent emotion. In the second stage, the descriptions are used as contextual information and, along with the image input, are used to train a transformer-based architecture.
  arXiv  Detail & Related papers  (2024-04-10T15:09:15Z)
• OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition [79.852642726105]
  We propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically, we devise a universal model, called Omni, which can simultaneously handle three typical visually-situated text parsing tasks. In Omni, all tasks share the unified encoder-decoder architecture, the unified objective point-conditioned text generation, and the unified input representation.
  arXiv  Detail & Related papers  (2024-03-28T03:51:14Z)
• Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding [22.00873805952277]
  WebLM is a multimodal pre-training network designed to address the limitations of solely modeling text and structure modalities of HTML in webpages. We propose several pre-training tasks to model the interaction among text, structure, and image modalities effectively. Empirical results demonstrate that the pre-trained WebLM significantly surpasses previous state-of-the-art pre-trained models across several webpage understanding tasks.
  arXiv  Detail & Related papers  (2024-02-28T11:50:36Z)
• Unifying Vision, Text, and Layout for Universal Document Processing [105.36490575974028]
  We propose a Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. Our method sets the state-of-the-art on 9 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites.
  arXiv  Detail & Related papers  (2022-12-05T22:14:49Z)
• Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding [58.70423899829642]
  We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding. We show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains.
  arXiv  Detail & Related papers  (2022-10-07T06:42:06Z)
• Modelling the semantics of text in complex document layouts using graph transformer networks [0.0]
  We propose a model that approximates the human reading pattern of a document and outputs a unique semantic representation for every text span. We base our architecture on a graph representation of the structured text, and we demonstrate that not only can we retrieve semantically similar information across documents but also that the embedding space we generate captures useful semantic information.
  arXiv  Detail & Related papers  (2022-02-18T11:49:06Z)
• TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance [15.72669617789124]
  Scene text recognition (STR) is an important bridge between images and text. Recent methods use a frozen initial embedding to guide the decoder to decode the features to text, leading to a loss of accuracy. We propose a novel architecture for text recognition, named TRansformer-based text recognizer with Initial embedding Guidance (TRIG)
  arXiv  Detail & Related papers  (2021-11-16T09:10:39Z)
• VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs [103.99315770490163]
  We present a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. Experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks.
  arXiv  Detail & Related papers  (2021-01-28T15:22:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.