PDF Decoder

This tool decodes PDFs by parsing internal objects to expose text, images, fonts, and metadata in a structured form. It targets data engineers, researchers, compliance teams, and content managers who need reliable access to PDF content for indexing, analysis, or ingestion into data lakes.
It supports both native text PDFs and scanned documents via OCR when enabled, and it preserves page boundaries and basic structure to support downstream parsing.
Conceptually, the tool accepts a PDF file, optionally a specific page range, and an output format preference, then runs a parser to extract content by logical blocks and pages. The output is organized into text blocks, an image set (as base64 or binary), and metadata, with the option to preserve simple table-like structures. The system emphasizes deterministic results for repeat runs.
Unique value comes from delivering consistent, machine-readable exports without rendering the document visually, enabling automated workflows, lineage tracking, and content comparisons across versions. Use cases include contract review automation, research paper data capture, and archival metadata inventory.
Core features include file ingestion, text extraction, image extraction, metadata capture, optional OCR, and page-range support. Outputs support JSON and plain text formats, with optional base64-encoded images. The tool operates in privacy-preserving environments and can be integrated into pipelines via API.