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Document AI AND Information extraction

Common descendants

2D-NLP

17 Documents

peft/examples/token_classification/peft_lora_token_cls.ipynb at main · huggingface/peft

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2023-08-27 About

Data-Efficient Information Extraction from Documents with Pre-Trained Language Models

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2023-02-14 About

[2111.15664] OCR-free Document Understanding Transformer

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2023-02-13 About

Revolutionizing Document AI with Multimodal Document Foundation Models - Microsoft Research

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2023-01-30 About

Transformers-Tutorials/LayoutLMv3 at master · NielsRogge/Transformers-Tutorials

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2023-01-17 About

[Tutorial] How to Train LayoutLM on a Custom Dataset with Hugging Face

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2023-01-09 About

LayoutLM Explained

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2022-12-21 About

Document AI: LiLT a better language agnostic LayoutLM model

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2022-11-22 About

LayoutLM

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LayoutLM

2022-10-04 About

[1912.13318] LayoutLM: Pre-training of Text and Layout for Document Image Understanding

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2022-10-04 About

Document AI: Fine-tuning LayoutLM for document-understanding using Hugging Face Transformers

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2022-10-04 About

A framework for designing document processing solutions

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2022-09-02 About

Philip Vollet sur Twitter : "Extracting information from PDFs or scanned documents is still a challenge! Use the @huggingface LayoutLMv3 model and Prodigy..."

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2022-09-02 About

Ankur Goyal sur Twitter : "DocQuery, a new #opensource query engine for analyzing documents using large language models (LLMs)..."

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2022-09-01 About

[1903.11279] Graph Convolution for Multimodal Information Extraction from Visually Rich Documents

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2020-06-16 About

Representation Learning for Information Extraction from Form-like Documents – Google Research

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2020-06-15 About

Google AI Blog: Extracting Structured Data from Templatic Documents (2020)

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[About this paper](doc:2020/06/representation_learning_for_inf)

Templatic documents (eg. invoices): such documents do not contain “natural
language” but
instead resemble forms, with data often presented in tables

> an approach that **uses knowledge of target field types to identify
candidate fields**. These are then scored using **a neural network that
learns a dense representation of each candidate using the words in its
neighborhood**. Experiments on two corpora (invoices and receipts) show
that we’re able to generalize well to unseen layouts.
>
> An understanding of the **two-dimensional layout of text**
on the page is key to understanding such documents. On the other hand,
treating this purely as an image segmentation problem makes it difficult
to take advantage of the semantics of the text.
>
> Our approach to this problem allows developers to train and deploy an
extraction system for a given domain (like invoices) using **two inputs — a
target schema (i.e., a list of fields to extract and their corresponding
types) and a small collection of documents labeled with the ground truth
for use as a training set**

- The input document is first run through an [OCR service](doc:2020/06/detecter_le_texte_dans_les_fich).
- a candidate generator identifies spans of text in the OCR output that might correspond to
an instance of a given field (uses pre-existing
libraries associated with each field type)
- Each candidate is then scored using a neural
network (that is trained as a binary classifier)

2020-06-15 About