Patent PDF Samples with Extracted Structured Data
contributors: Google Patents
tags: machine learning, OCR, document recognition, benchmarking, validation
terms of_use: CC BY 4.0
documentation: At site
description: The dataset consists of PDFs in Google Cloud Storage from the first page of select US and EU patents, and BigQuery tables with extracted entities, labels, and other properties, including a link to each file in GCS. The structured data contains labels for eleven patent entities (patent inventor, publication date, classification number, patent title, etc.), global properties (US/EU issued, language, invention type), and the location of any figures or schematics on the patent's first page. The structured data is the result of a data entry operation collecting information from PDF documents, making the dataset a useful testing ground for benchmarking and developing AI/ML systems intended to perform broad document understanding tasks like extraction of structured data from unstructured documents. This dataset can be used to develop and benchmark natural language tasks such as named entity recognition and text classification, AI/ML vision tasks such as image classification and object detection, as well as more general AI/ML tasks such as automated data entry and document understanding. Google is sharing this dataset to support the AI/ML community because there is a shortage of document extraction/understanding datasets shared under an open license.
last edit: Thu, 27 Jul 2023 09:52:46 GMT