PDF Inspector

An open-source tax-document page classifier built with TypeSafe Jev. It extracts text from PDF pages, then uses decision-model requests to classify each page by form and page kind, returning probability distributions, a form identifier, confidence scores, and a gate indicating whether the result meets the configured confidence threshold. Its generated JSON criteria file describes IRS forms using data extracted from IRS form PDFs; a second request handles schedules for selected corporate and foreign forms. The package includes PDF helpers based on Poppler, supports text-layer input, and requires a TypeSafe API key for the Jev backend. It does not train or host a model, is limited to English federal-form classification, requires OCR for scanned pages, and is distributed under Apache-2.0 with separate licensing for IRS-derived data.

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.

Rust is a systems programming language and toolchain developed by the Rust project. Its compiler, standard library, documentation, package manager and build tool Cargo, formatter rustfmt, linter Clippy, and editor support are maintained in the main repository. The language uses a rich type system and ownership model to enforce memory and thread safety at compile time, while targeting fast, memory-efficient software for critical services, embedded devices, and integration with other languages. Rust is primarily distributed under the MIT and Apache 2.0 licenses, with portions under BSD-like licenses.

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.