25 tools
PostScript and PDF interpreter/renderer from Artifex: converts PostScript/PDF to images, text, DOCX, PCL, and XPS; compresses PDFs while preserving quality; converts to/validates PDF/A; adds an OCR text layer to scanned pages; imposition (N-up), ICC color management, and ZUGFeRD/Factur-X e-invoicing support.
C#Open-source virtual (network) printer for Windows: print-to-PDF, PDF/A-1b/2b/3b, PDF/X, PDF/Image, PNG/JPEG/TIF/text output; OCR; password protection with up to 256-bit AES encryption; and multi-user/enterprise deployment support.
Go library/CLI/HTTP service that converts PDF, DOC, DOCX, XML, HTML, RTF, ODT, Pages, and image files to plain text and metadata (JSON), with optional OCR support (via Tesseract, built with the `ocr` tag) for image-based input.
Go wrapper around the Tesseract OCR C++ library: sets an input image and returns the OCR'd text, with support for multiple languages via trained-data files.
JavaSelf-hostable PDF toolkit with 50+ tools: edit, merge, split, sign, redact, and convert PDFs; OCR scanned pages; compress files; validate signatures; apply watermarks; and automate workflows via no-code pipelines, a REST API, and webhooks. Offered as a browser-based UI, desktop client, or self-hosted server (incl. air-gapped deployment).
JavaContent-analysis toolkit that detects file types and extracts text and metadata from over a thousand file formats (including PDF, DOCX, PPTX, XLSX) through a single parsing interface; supports OCR (via Tesseract), language detection, named-entity recognition, and parsing of embedded documents/attachments. Available as a Java library, CLI (tika-app), or REST server (tika-server).
JavaScriptSet of tools (per its npm registry listing) that convert PDF files to usable formats for data processing: extracting text from searchable PDFs and performing OCR on scanned/image-only PDFs. Note: its GitHub source repository (nisaacson/pdf-extract) now returns 404 — likely deleted or made private; this description is sourced from the published npm package metadata instead.
Node.js module (CLI + API) that extracts text from HTML, PDF, DOC/DOCX, ODT, RTF, XLS/XLSX/XLSB/XLSM, CSV, ODS, PPTX/ODP, XML, Markdown, EPUB, and image formats (PNG/JPG/GIF via Tesseract OCR); wraps external tools (pdftotext, antiword, tesseract, unrtf) and supports password-protected PDFs and configurable OCR language.
JavaScriptOCR/document-ingestion library (Node/TypeScript and Python implementations) that converts PDFs, Word/Excel/PowerPoint files, and images to page images, then sends each to a vision-language model (OpenAI, Azure OpenAI, AWS Bedrock, Google Gemini, or Vertex AI) to produce Markdown, preserving tables/charts; supports structured JSON-schema extraction, page selection, concurrent processing, and orientation/edge correction.
PythonConverts scanned-book PDFs to Markdown or EPUB using OCR (DeepSeek OCR locally, or DeepSeek/Baidu vendor backends), recognizing complex structure including tables and formulas; extracts body text while filtering headers/footers, preserves footnotes/images/assets, auto-generates an EPUB table of contents (optionally LLM-enhanced), and offers configurable table (HTML/image) and formula (MathML/SVG/image) rendering. Runs fully offline as of v1.0.0.
PythonConverts PDFs, images, PPTX, DOCX, XLSX, HTML, and EPUB to Markdown, JSON, HTML, or chunks, preserving reading order: reconstructs tables (heuristics with VLM fallback for complex cases), recognizes math/equations as LaTeX, extracts and saves images, performs multilingual OCR via the Surya VLM, strips headers/footers/artifacts, and can optionally use an LLM to improve accuracy.
PythonExtracts structured data from invoice PDFs: pulls text via pdftotext, pdfminer, or OCR (tesseract/tesseract4/Google Cloud Vision), then matches fields (invoice number, date, amount, line items) using regex patterns defined in per-supplier YAML templates; exports to CSV, JSON, XML, or renames files based on extracted content; supports batch folder processing.
Adds a searchable OCR text layer beneath page images and produces a validated PDF/A file: deskews crooked pages, optionally cleans images pre-OCR, optimizes/compresses images, recognizes 100+ languages via Tesseract, rotates misrotated pages, edits output metadata, parallelizes across CPU cores, and processes multi-thousand-page files in place.
PythonPersonal document manager (Linux/Windows) for scanning, OCR'ing, indexing, and searching paper documents and PDFs; the project's own repository topics list OCR, scanning, PDF support, indexing, and document management as its core feature areas.
PythonSimplified fork/UI re-think of Mayan EDMS aimed at home users: scans, indexes, and archives paper documents for later retrieval, with a reduced feature set compared to the underlying Mayan EDMS platform.
Python bindings for MuPDF covering text extraction (plain, rich dictionary with font/size/color/bbox, HTML/XML, raw blocks, table-to-Markdown); embedded image extraction and high-DPI page rendering to Pixmaps; Tesseract-based OCR (100+ languages); reading/writing annotations (highlights, notes, stamps, ink) and redactions (with permanent apply); AcroForm reading and filling; page insert/delete/reorder, merge/split, and metadata editing; PDF creation and Markdown-to-PDF conversion; password protection/RC4/AES encryption; hyperlink and cross-reference handling; and outline/bookmark read-write.
PythonExtracts higher-quality OCR text from poorly scanned PDFs and images (PDF, PNG, JPG, BMP, TIFF) by fixing common scan errors: crops to estimated text regions, deskews tilted pages, and optimizes images before running Tesseract OCR; supports bulk processing (splitting multi-page PDFs) and language-code hints for accuracy.
OCR-free document-understanding transformer (PyTorch/Hugging Face) that classifies document types, extracts structured information (e.g. from receipts), answers questions about document content, and reads text sequences directly from document images without a separate OCR engine.
Neural OCR model (Meta/Facebook Research) that parses academic PDF papers into Markdown (.mmd), understanding LaTeX math and tables; supports single or batch PDF processing, page-range selection, and failure-detection heuristics for problematic pages.
PythonConverts PDF, DOCX, PPTX, XLSX, images, and web pages into Markdown or JSON: strips headers/footers/page numbers while preserving structure, converts formulas to LaTeX and tables to HTML, OCRs scanned/garbled PDFs in 109 languages, handles single/multi-column and complex layouts (incl. cross-page table merging), and extracts images with captions. Offers CLI, FastAPI, and Gradio WebUI, plus Python/Go/TypeScript SDKs; builds on PDF-Extract-Kit's models.
PythonModel toolbox for extracting content from complex PDFs: layout detection (images/tables/text/titles/formulas), formula detection and LaTeX recognition, OCR with text location, and table recognition to LaTeX/HTML/Markdown. Provides the underlying models that MinerU builds its PDF-to-Markdown application on top of.
PythonOpen-source ETL library that converts complex documents (including PDFs) into clean, structured data for LLM ingestion: PDF text extraction, OCR on scanned pages (via tesseract-ocr/poppler-utils), document layout detection, and partitioning into logical elements via a single `partition()` auto-detection function.
PythonDeep-learning toolkit for document image layout analysis: detects layout elements (text blocks, titles, tables, figures) using pre-trained models, filters/crops regions by spatial location, loads layout data from JSON/CSV/PDF, runs OCR on detected regions, and visualizes detected layouts.
PythonApache-2.0 orchestration framework for document AI: combines layout detection and table recognition (via Detectron2/Transformers), OCR (Tesseract, DocTr, or AWS Textract), and document/token classification (LayoutLM family, LiLT, BERT-style models) into configurable pipelines for scanned documents and PDFs.
PythonPipeline library for extracting information from unstructured documents with low memory/CPU overhead: PDF table extraction, image analysis with OCR, document question-answering via LLM integration, vector index creation, and support for most common document formats.