PDF Xtract has always offered powerful AI-driven element extraction through its web app at pdf-xtract.com. Today we're spotlighting the desktop version — a standalone application that brings the same extraction engine to your local machine, with no internet dependency, no usage caps, and no per-page cost after purchase.
Why a Desktop App?
There are good reasons to want local processing. Sensitive documents — legal filings, medical records, internal financial reports — shouldn't leave your machine. High-volume workflows benefit from a flat annual cost rather than per-page billing. Offline environments (air-gapped systems, aircraft, locations with unreliable connectivity) are fully supported. The desktop app addresses all of these scenarios.
What It Does
The desktop app runs the same YOLO-based detection model used by the web service. It identifies eleven element categories in a single pass — images, tables, text blocks, titles, section headers, captions, footnotes, mathematical expressions, list entries, page headers, and footers — and exports each detected region as a cropped image at your chosen resolution (up to 800 DPI). Optional Tesseract OCR integration extracts text from detected elements.
Platform Support
- macOS (Apple Silicon M-series and Intel x86_64) — .dmg installer
- Windows 10 and later — .exe setup installer
- Linux (Debian/Ubuntu) — .deb package
- Linux (any distribution) — .AppImage portable binary
- Optional: NVIDIA CUDA and AMD ROCm GPU backends for Linux (compiled features)
GPU Acceleration on Linux
Linux builds of the desktop app can be compiled with optional GPU inference backends. The ort-cuda feature enables NVIDIA GPU acceleration via the CUDA execution provider, and ort-rocm enables AMD GPU acceleration via ROCm. CPU inference remains available as a fallback if a GPU provider cannot initialize. For most users on modern hardware, the CPU version delivers fast extraction — GPU support is primarily relevant for very high-volume batch workloads on Linux servers.
Pricing
The desktop app is available under an annual license. Compared to cloud-based alternatives — Adobe PDF Extract API starts at $25,000/year at scale; Azure Document Intelligence charges $1.50–$30 per 1,000 pages — the desktop app's unlimited model makes economics straightforward for any organization processing more than a few thousand pages per month.