Adobe PDF Extract API
Unlock the structure and content elements of any PDF with a web service powered by Adobe Sensei's machine learning
Included with PDF Services API
Key Features of Adobe PDF Extract API
Comprehensive Content Extraction
Extract all PDF document elements including text, tables, and images within a structured JSON file to enable a variety of downstream solutions.
Document Structure Understanding
Classify text objects such as headings, lists, footnotes, and paragraphs that may span multiple columns or pages. Capture text fonts and styles, positioning, and the natural reading order of all objects.
Highly Accurate
Results
Adobe Sensei AI technology delivers highly accurate data extraction across a broad range of document types – both native and scanned PDFs – without requiring custom ML templates or model training.
Platform
Agnostic
Adobe’s PDF Extract API is RESTful and can be used to seamlessly integrate with any cloud platform or on-premise application.
Adobe PDF Extract API Use Cases
Content Processing
Quickly and accurately extract data and context from native and scanned PDFs to automate downstream processes using technologies like Robotic Process Automation (RPA) and Natural Language Processing (NLP).
Data Analysis
Extract data from complex tables including cell data, column and row headers, and table properties for use in machine learning models, analysis, or storage.
Content Republishing
Republish the content in PDF documents across different media, languages, and formats by extracting not just data but also structural context, text and table formatting, and reading order.
How it Works
Advanced machine learning and artificial intelligence parse complex document content for reuse in a variety of critical downstream processes
Adobe PDF Extract API is powered by Adobe Sensei industry-leading artificial intelligence (AI) and machine learning (ML). The technology enables a rich understanding of documents, such as the identification of elements, including position and connections relative to other elements. In addition, it can determine reading order. These and other capabilities ensure the most comprehensive output of structured content.
Extracted content is output in a structured JSON file – with tables optionally included as CSV or XLSX files and images saved as PNG files – so developers can easily store, analyze, and manipulate the data in a variety of downstream systems. Examples include databases, systems of record, CRM, ERP, NLP, RPA as well as machine learning models and analytic tools.
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