AWS Machine Learning Blog

NOVEMBER 22, 2023

The IDP Well-Architected Custom Lens is intended for all AWS customers who use AWS to run intelligent document processing (IDP) solutions and are searching for guidance on how to build a secure, efficient, and reliable IDP solution on AWS. This post focuses on the Reliability pillar of the IDP solution.

IDP 102

IDP Automation Machine Learning ML 102

ODSC - Open Data Science

OCTOBER 27, 2023

With Intelligent Document Processing (IDP) leveraging artificial intelligence (AI), the task of extracting data from large amounts of documents with differing types and structures becomes efficient and accurate. The following diagram is how we visualize these IDP phases.

IDP 98

IDP LLM Large Language Models Data Science 98

AWS Machine Learning Blog

MARCH 29, 2023

The postprocessing component uses bounding box metadata from Amazon Textract for intelligent data extraction. The postprocessing component is capable of extracting data from complex, multi-format, multi-page PDF files with varying headers, footers, footnotes, and multi-column data.

ML 83

ML Metadata Data Ingestion Data Extraction 83

AWS Machine Learning Blog

NOVEMBER 13, 2024

The market size for multilingual content extraction and the gathering of relevant insights from unstructured documents (such as images, forms, and receipts) for information processing is rapidly increasing. We specifically used the Rhubarb Python framework to extract JSON schema -based data from the documents.

IDP 95

IDP Machine Learning Python ML 95

AWS Machine Learning Blog

JULY 18, 2024

Enterprise customers can unlock significant value by harnessing the power of intelligent document processing (IDP) augmented with generative AI. By infusing IDP solutions with generative AI capabilities, organizations can revolutionize their document processing workflows, achieving exceptional levels of automation and reliability.

IDP 128

IDP Generative AI Data Extraction Automation 128

AWS Machine Learning Blog

DECEMBER 17, 2024

Developers face significant challenges when using foundation models (FMs) to extract data from unstructured assets. This data extraction process requires carefully identifying models that meet the developers specific accuracy, cost, and feature requirements.

Automation 106

Automation IDP Generative AI Data Extraction 106