Transforms PDFs, documents, and images into structured, enriched JSON, Markdown, or CSV data.
Turn unstructured PDF, document, and image formats into clean, structured data sets.
Parsr is a minimal-footprint toolchain that ingests PDFs, documents, images, and emails to extract structured and label-enriched information. It suits developers and data analysts who need to convert messy document layouts into formats like JSON, Markdown, or Pandas DataFrames.
The tool performs document cleaning, hierarchy regeneration for words and paragraphs, and detects headings, tables, lists, table of contents, and page numbers. It exposes an API that you can interact with via a Python client, Jupyter notebooks, or a dedicated GUI.
It suits data scientists and developers building automated document processing pipelines. It does not suit compliance professionals seeking an out-of-the-box GRC platform, as it requires integration work and technical setup.
Processes PDFs, images, docx files, and emails into structured outputs.
Detects words, lines, paragraphs, headings, tables, and lists automatically.
Generates readily available data in JSON, Markdown, CSV, or TXT.
Includes a Python client, Jupyter notebook demo, and a Docker-based GUI viewer.
Parsr is free to use under its license. The only cost is running and hosting the required infrastructure yourself.
880 open remote AI-training roles, updated November 15, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
No, the project is officially no longer maintained, and security patches are not being applied.
It outputs structured data in JSON, Markdown, CSV, Pandas DataFrames, or TXT formats.
You can pull and run the axarev/parsr-ui-localhost Docker image alongside the API container.
See Zekai first in Google