doc7

Docling is an open-source document-processing toolkit from the Docling project for preparing files for generative-AI applications. It parses formats including PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, plain text, audio, and video, and represents the results in a unified DoclingDocument structure. For PDFs, it analyzes page layout, reading order, headings, tables, code, formulas, images, and scanned content through OCR. Audio and video inputs can be processed with automatic speech-recognition models; video parsing produces an ASR transcript and representative keyframes. Parsed content can be exported as Markdown, HTML, WebVTT, DocLang, DocTags, or lossless JSON. Docling supports local and air-gapped execution, a command-line interface, Python usage, an API server named docling-serve, and an MCP server for connecting agents. The project also provides integrations for LangChain, LlamaIndex, CrewAI, and Haystack. It is installable with pip and runs on macOS, Linux, and Windows on x86_64 and arm64 systems.

anydoc is an open-source Rust library from Firecrawl that converts Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF files into GitHub-Flavored Markdown. It uses a shared document model and provides Node.js, Python, browser WebAssembly, and Rust interfaces, along with a command-line interface that reads files or standard input and writes Markdown to standard output or a file. The library can return either Markdown or its intermediate document model, which carries embedded assets. A browser demo runs the WebAssembly build locally, while hosted OCR through Firecrawl Parse can process scanned PDF pages that anydoc cannot read by itself. It is also distributed as an agent skill compatible with agent tools such as Claude Code, Codex, Cursor, and OpenCode.

MarkItDown is a Microsoft Python utility and command-line tool that converts files and office documents into Markdown for use with large language models and text-analysis pipelines. It aims to preserve document structure such as headings, lists, tables, links, and other content rather than produce high-fidelity human-facing document conversions. Its built-in converters handle formats including PDF, PowerPoint, Word, Excel, images, audio, HTML, CSV, JSON, XML, ZIP files, YouTube URLs, and EPUBs. It can be used through a Python API, command line, Docker, or third-party plugins; optional integrations support OCR with an LLM vision client, Azure Document Intelligence, and Azure Content Understanding, including multimodal conversion and YAML front-matter field extraction. The repository notes that conversion performs I/O with the current process's privileges, so untrusted inputs must be sanitized and the narrowest conversion method should be used.

Umi-OCR is free, open-source, offline OCR software for Windows 7 x64 and later. It recognizes text from screenshots, pasted images, batches of local images, and PDF documents, and can save batch results as TXT, JSONL, Markdown, or CSV files. Its OCR post-processing can merge lines into natural paragraphs, preserve code-block indentation, handle vertical text, and ignore configured rectangular regions such as watermarks or logos. It also includes screenshot, image, and drag-and-drop scanning for QR codes and barcodes, plus QR-code generation. The application can switch between OCR engines through plugins and provides command-line and HTTP API interfaces. The release is distributed as a portable archive or self-extracting archive, requiring no installation or network connection to run. The project is hosted on GitHub and supports multiple interface languages and themes.

DocJev is an open-source Python library, CLI, and local app for classifying and splitting PDF, DOCX, and PPTX files with user-written natural-language rules. LiteParse extracts page text locally, then the hosted Jev decision engine assigns document categories or identifies boundaries between component documents; optional LlamaParse tiers provide cloud OCR for difficult inputs. Classification returns a category, probabilities, and review flags. Splitting returns ordered categories and contiguous page ranges, with optional export of each segment as a PDF and review reasons for uncertain boundaries. DOCX and PPTX processing requires LibreOffice, and inference is not offline because Jev is a hosted service. The project is distributed under the Apache-2.0 license.