LiteParse

LlamaParse is LlamaIndex's document-processing platform for converting complex documents such as PDFs, scans, Office files, tables, charts and schematics into structured Markdown or JSON for indexing and retrieval by applications and AI agents. Its API also provides schema-based JSON extraction, document classification, splitting and indexing capabilities, with SDKs for Python, TypeScript, Go and Java.

PDF Inspector is an open-source Rust library by Firecrawl for classifying PDF documents and extracting position-aware text without OCR. It samples content streams to classify documents as text-based, scanned, image-based, or mixed, returns confidence scores and per-page OCR-routing information, and detects broken font encodings. Its extractor uses font information and X/Y coordinates to determine reading order, including multi-column and right-to-left layouts, and converts content to Markdown with headings, lists, code blocks, tables, formatting, links, and page breaks. Native Rust and CLI consumers can opt into selective OCR for pages that need it; the Python and Node.js bindings can run the same workflow, while browser WebAssembly provides local parsing without a server round trip. The project also supports CID font decoding and shares a single parsed document between classification and extraction.

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.

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.

TaxHacker is a self-hosted AI accounting application for freelancers, independent developers, and small businesses. It processes receipt photos, invoice PDFs, bank statements, and other documents with a configurable LLM, extracting dates, merchants, amounts, taxes, product names, and line items into a structured, Excel-like database. It can categorize transactions, split invoice items into separate transactions, and use custom fields with user-defined prompts; supported model backends include OpenAI, Google Gemini, Mistral, and local OpenAI-compatible endpoints such as Ollama, LM Studio, vLLM, and LocalAI. The application detects document currencies and converts transactions using historical exchange rates from the transaction date, including cryptocurrency support, and provides filtering, multi-project organization, import/export, and custom categories. The repository describes the project as being in early development and advises using it at one's own risk.