layoutlmv2-base-uncased

Maintainer: microsoft - Last updated 9/6/2024

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Run this modelRun on HuggingFace
API specView on HuggingFace
Github linkNo Github link provided
Paper linkNo paper link provided

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Model overview

LayoutLMv2 is a multimodal AI model developed by Microsoft that is designed for understanding visually-rich document understanding. It builds upon the original LayoutLM model by introducing new pre-training tasks to better model the interaction between text, layout, and images. This improved version outperforms strong baselines and achieves new state-of-the-art results on a variety of document understanding tasks.

Compared to similar models like [object Object], LayoutLMv2 is a monolingual English model, while LayoutXLM adds support for multilingual document understanding. [object Object] and [object Object] represent the latest advancements in the LayoutLM series, with a unified architecture and training objectives for both text-centric and image-centric document AI tasks.

Model inputs and outputs

LayoutLMv2 takes in multimodal document data, including the text content, layout/formatting information, and images. The model can then be used to perform a variety of downstream tasks, such as document classification, information extraction, and visual question answering.

Inputs

  • Text content of the document
  • Bounding box coordinates and other layout/formatting features
  • Document images

Outputs

  • Task-specific outputs, such as:
    • Document classification labels
    • Extracted entities or key information
    • Answers to visual questions about the document

Capabilities

LayoutLMv2 excels at understanding the complex relationships between text, layout, and visual elements in documents. For example, it can accurately extract structured information from forms and receipts by jointly modeling the text content and visual cues. It also achieves state-of-the-art performance on document visual question answering, where the model must reason about both the textual and visual aspects of the document.

What can I use it for?

LayoutLMv2 is a powerful tool for automating various document processing tasks, such as invoice and contract analysis, document classification, and information extraction. It can be particularly useful for companies dealing with visually-rich documents, as it can significantly improve the accuracy and efficiency of these operations compared to traditional approaches.

Things to try

One interesting aspect of LayoutLMv2 is its ability to handle non-consecutive tokens when extracting information from documents. Unlike many QA models that can only predict contiguous text spans, LayoutLMv2 can identify and extract relevant information even when it is spread across multiple locations on the page. This can be especially useful for tasks like address extraction, where the relevant information may be split across multiple lines.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Total Score

50

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layoutxlm-base

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Total Score

57

layoutxlm-base is a multilingual variant of the LayoutLMv2 model, developed by Microsoft's Document AI team. It is a multimodal pre-trained model for multilingual document understanding, aiming to bridge language barriers for visually-rich document processing. Experiment results show that layoutxlm-base significantly outperforms existing state-of-the-art cross-lingual pre-trained models on the XFUND dataset. Similar models developed by Microsoft include LayoutLMv3, a pre-trained multimodal Transformer for Document AI with unified text and image masking, and Multilingual-MiniLM-L12-H384, a small and fast pre-trained multimodal model for language understanding and generation. Model inputs and outputs Inputs Document images Multilingual text Layout/format information Outputs Representations for document understanding tasks such as form understanding, receipt understanding, and document visual question answering. Capabilities layoutxlm-base is designed for multimodal (text, layout, and image) document understanding, particularly in a multilingual setting. It can be fine-tuned for a variety of text-centric and image-centric document AI tasks, leveraging both textual and visual information. What can I use it for? You can fine-tune layoutxlm-base on your own document understanding tasks, such as document classification, information extraction, or question answering. The model's multilingual capabilities make it useful for processing documents in a diverse set of languages. Additionally, its multimodal nature allows it to effectively handle visually-rich documents like forms, receipts, and invoices. Things to try Try fine-tuning layoutxlm-base on your own document understanding dataset and compare its performance to other SOTA models like LayoutLMv2 or LayoutLMv3. Experiment with different fine-tuning approaches, such as adding task-specific layers or using different optimization strategies, to see how you can further improve the model's performance on your specific use case.

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layoutlmv3-base

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layoutlmv3-base is a pre-trained multimodal Transformer model for Document AI developed by Microsoft. It has a unified architecture that can be fine-tuned for both text-centric tasks, like form understanding and document visual question answering, as well as image-centric tasks such as document image classification and layout analysis. layoutlmv3-base builds on the previous LayoutLM models by introducing a unified text and image masking approach during pre-training, which makes it a general-purpose pre-trained model for visually-rich document understanding. Similar models include layoutlmv3-large, a larger version of the same architecture, and layoutlmv3-base-chinese, a version pre-trained on Chinese text and documents. Another related model is layoutxlm-base, a multilingual variant of LayoutLMv2 for cross-lingual document understanding. Model inputs and outputs Inputs Document images Document text Outputs Representations of the document text, layout, and visual elements, which can be used for a variety of downstream tasks. Capabilities layoutlmv3-base can be fine-tuned for tasks like form understanding, receipt parsing, and document visual question answering. It has shown strong performance on benchmarks like XFUND and EPHOIE. The model's unified architecture and training approach make it a versatile pre-trained model for working with visually-rich documents. What can I use it for? layoutlmv3-base can be used for a variety of document processing and understanding tasks, such as: Form understanding**: Extracting key information from forms and receipts. Document visual question answering**: Answering questions about the content and layout of documents. Document image classification**: Classifying the type of document (e.g., invoice, contract, resume). Document layout analysis**: Understanding the structure and organization of a document. These capabilities can be useful for automating document-heavy workflows, improving document search and retrieval, and extracting valuable insights from large collections of documents. Things to try One interesting aspect of layoutlmv3-base is its unified training approach, which combines text and image masking. This allows the model to learn rich multimodal representations that can be leveraged for a wide range of document-related tasks. Experimenting with different fine-tuning strategies and downstream applications can help uncover the full potential of this versatile pre-trained model.

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layoutlmv3-large

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Total Score

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LayoutLMv3 is a pre-trained multimodal Transformer for Document AI developed by Microsoft Document AI. The model has a simple unified architecture and training objectives that make it a general-purpose pre-trained model. LayoutLMv3 can be fine-tuned for both text-centric tasks, such as form understanding and document visual question answering, as well as image-centric tasks like document image classification and layout analysis. Model inputs and outputs Inputs Text and images from document-based tasks Outputs Predictions for various document-related tasks like document understanding, layout analysis, and visual question answering Capabilities LayoutLMv3 can handle a wide range of document-centric AI tasks through its unified text and image masking approach. The model has shown strong performance on benchmarks for tasks like receipt understanding, form parsing, and document visual question answering. Its versatile architecture makes it a powerful tool for automating various document processing workflows. What can I use it for? The general-purpose nature of LayoutLMv3 means it can be applied to many real-world document AI use cases. Some potential applications include: Automating financial document processing (e.g. invoice, receipt understanding) Accelerating legal document review and analysis Enhancing content understanding for digital publishing Powering intelligent document search and retrieval systems Things to try One interesting aspect of LayoutLMv3 is its ability to handle both text and visual information from documents. Developers could experiment with using the model for multimodal tasks that combine textual and visual cues, such as understanding the overall layout and structure of a document or answering questions that require interpreting both the text and visual elements.

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layoutlmv3-base-chinese

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Total Score

51

layoutlmv3-base-chinese is a pre-trained multimodal Transformer for Document AI developed by Microsoft's Document AI team. It has a unified text and image masking architecture that makes it a general-purpose model capable of both text-centric tasks like form understanding and image-centric tasks like document image classification. The simple unified design and training objectives allow layoutlmv3-base-chinese to excel across a variety of Document AI applications. Model inputs and outputs Inputs Text**: The model can take text input, such as document text. Images**: The model can also take images as input, such as document layout images. Outputs Text-centric tasks**: The model can output structured information like form fields or document understanding. Image-centric tasks**: The model can output image classifications or document layout analysis. Capabilities layoutlmv3-base-chinese demonstrates strong performance on both text-centric and image-centric Document AI tasks. For example, on the XFUND dataset for Chinese document understanding, it achieves an F1 score of 92.02%. On the EPHOIE dataset for receipt understanding, it reaches near-perfect scores across various metrics like name, school, and grade. What can I use it for? layoutlmv3-base-chinese can be used for a variety of Document AI applications, such as: Form understanding**: Extracting structured information from forms. Receipt understanding**: Analyzing receipts to extract key details like total amount, items purchased, etc. Document visual question answering**: Answering questions about the content and layout of documents. Document image classification**: Classifying documents into categories like invoices, contracts, reports, etc. Document layout analysis**: Detecting and understanding the layout elements of documents. Things to try One interesting thing to try with layoutlmv3-base-chinese is fine-tuning it on domain-specific document datasets. Since the model has a general-purpose architecture, it can likely be adapted to work well on various types of documents beyond the benchmarks it was evaluated on. Experimenting with fine-tuning the model on your own document datasets could uncover valuable insights and applications.

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