31 tools
Content-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).
Open-source virtual (network) printer for Windows that turns any application's Print dialog into a PDF-generation pipeline. Outputs PDF/A-1b/2b/3b, PDF/X, PDF/Image, PNG, JPEG, TIFF, and plain text; runs OCR on the printed content; and password-protects output with up to 256-bit AES encryption. Built with multi-user and enterprise deployment scenarios in mind, including centrally managed print-queue configurations across a network.
Cog/Replicate-deployable packaging of Marker (see the separate Marker entry), converting PDFs — and EPUB, MOBI, XPS, FB2 — to Markdown via layout analysis and OCR, with table formatting and LaTeX-equation conversion carried over from the underlying Marker engine. Exists as its own entry because it's a distinct deployable artifact (a Cog container image runnable on Replicate's infrastructure) rather than a different tool — the actual conversion logic is Marker's.
Apache-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.
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.
Go library, CLI, and HTTP service that converts PDF, DOC, DOCX, XML, HTML, RTF, ODT, Pages, and image files to plain text plus metadata (as JSON), aiming to be a single conversion entry point across many document formats rather than a PDF-specific tool. Optional OCR support (via Tesseract, enabled with the `ocr` build tag) handles image-based input. Its multi-format scope suits ingestion pipelines that need to normalize mixed document types into plain text.
Document-conversion toolkit that parses PDFs (and Office/image formats) into a unified structured representation, with layout analysis, OCR (EasyOCR by default), table structure recognition, and optional vision-language-model image description; exports to Markdown, HTML, or JSON.
PythonOCR-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.
PostScript and PDF interpreter/renderer from Artifex — the reference engine that many other PDF tools build on or shell out to under the hood. Converts PostScript and PDF to raster images, plain text, DOCX, PCL, and XPS; compresses PDFs while preserving visual quality; converts to and validates against the PDF/A archival standard; and adds a searchable OCR text layer to scanned pages. Also handles imposition (N-up page layout), ICC color management, and ZUGFeRD/Factur-X e-invoicing embedding.
Go wrapper around the Tesseract OCR C++ library via cgo bindings: feed it an input image and it returns the recognized text, with support for over 100 languages via Tesseract's trained-data files. Requires Tesseract itself (and its language data) installed as a system dependency, since gosseract only provides the Go-side binding rather than a self-contained OCR engine. A straightforward way to add OCR to a Go application without shelling out to the `tesseract` CLI directly.
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.
Create and manipulate PDFs, merge documents, digitally sign, create and fill AcroForm fields, generate PDF/A and PDF/UA archival/accessible output, encrypt (including FIPS-compliant crypto), generate barcodes, embed SVG, and redact content — the core feature set of the modern, actively-developed iText line. Separate official add-ons extend it further: pdfOCR (Tesseract- or ONNX-based optical character recognition) and pdfHTML (HTML/CSS-to-PDF conversion), both maintained by the same organization rather than third-party plugins.
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.
Converts 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.
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.
PythonNeural 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.
PythonAdds 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.
Simplified fork and UI rethink of Mayan EDMS aimed at home users rather than enterprise document-management deployments: scans, indexes, and archives paper documents for later retrieval, with a deliberately reduced feature set compared to the underlying Mayan EDMS platform it forked from. Predates and is unrelated to the now much more widely used Paperless-ngx project, despite the similar naming and overlapping goal of turning a scanner into a searchable document archive.
Personal 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.
RubyZero-runtime-dependency document-parsing gem: parses PDFs, DOCX, XLSX, and images (with OCR), statically linking MuPDF and Tesseract directly into the compiled gem at install time, so no system libraries or external tools need to be separately installed. This trades a heavier, slower install (native compilation of two large C libraries) for zero runtime dependency headaches afterward — no version mismatches with a system-installed MuPDF or Tesseract to worry about.
Converts 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.
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.
Model 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.
PythonSelf-hostable enterprise search and agentic-workflow platform that unifies business data (PDF, Word, Excel, PowerPoint, and more) into a searchable knowledge layer: parses PDFs via pdfplumber, with optional Docling-based multimodal parsing and LLM-based OCR for scanned documents.
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.
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.
JavaScriptOCR and text-extraction library for images and PDFs: extracts existing text from text-native PDFs, runs OCR on image-native PDFs/scans, and can insert a text layer into an existing PDF to make it searchable. Usable from the browser, Node.js, or its own CLI; a hosted GUI is available separately at scribeocr.com.
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).
JavaScriptNode.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.
Open-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.
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.