Sahaf

Local PDF & EPUB to Markdown converter with automatic digital/scanned detection, OCR support, smart splitting, and page-range selection. Converts books to clean, self-contained Markdown files with embedded images using Marker (95.67% accuracy) and Surya OCR (90+ languages). No cloud APIs — runs entirely on your hardware.

sahaf_demo.mp4

Features

• PDF & EPUB support — handles both formats natively
• Automatic PDF classification — detects digital, scanned, or mixed PDFs via PyMuPDF
• High-accuracy conversion — Marker with 95.67% benchmark accuracy
• Built-in OCR — Surya OCR supports 90+ languages (Turkish, English, Arabic, etc.)
• Page/chapter range selection — convert only a specific section of the book (e.g. pages 19-88)
• Smart splitting — split output into N parts, cutting at heading/paragraph boundaries instead of mid-sentence
• Self-contained output — images embedded as base64 directly in Markdown, no separate files
• Split preview — see exactly how parts will be divided before downloading
• Bilingual UI — Turkish / English interface with one-click toggle
• Dark/light theme — lavender-toned design, persistent toggle
• Drag & drop UI — clean single-page web interface

Install

pip install sahaf

Or from source:

git clone https://github.com/arikusi/sahaf.git
cd sahaf
pip install -e .

Marker models (~2-3GB) are downloaded automatically on first conversion.

Quick Start

sahaf

Open http://localhost:8000 in your browser.

How It Works

1. Upload — drag & drop a PDF or EPUB file
2. Classify — PyMuPDF analyzes PDF type; EPUB chapters are counted
3. Select range (optional) — pick specific pages or chapters to convert
4. Convert — Marker processes PDF; ebooklib + markdownify handles EPUB
5. Split (optional) — choose how many parts to split the output into
6. Download — get a single .md or a ZIP with split parts, all images embedded inline

API

Method	Path	Description
POST	/api/upload	Upload PDF/EPUB, returns task_id
GET	/api/classify/{task_id}	Detect PDF type + page count, or EPUB chapter count
POST	/api/convert/{task_id}?page_from=&page_to=	Start conversion (optional page range)
GET	/api/status/{task_id}	Poll conversion progress
GET	/api/result/{task_id}	Get markdown + image list
GET	/api/download/{task_id}	Download .md with embedded images
GET	/api/download/{task_id}/zip?parts=N	Download ZIP with N split .md files
GET	/api/split-preview/{task_id}?parts=N	Preview split structure before download

Tech Stack

• Backend: FastAPI + Uvicorn
• PDF Classification: PyMuPDF
• PDF Conversion: Marker (marker-pdf) + Surya OCR
• EPUB Conversion: ebooklib + markdownify
• Smart Splitting: Custom algorithm — heading/HR/paragraph boundary detection
• Frontend: Vanilla HTML/CSS/JS + marked.js
• i18n: TR/EN with client-side toggle

Requirements

• Python 3.10+
• 4-6GB RAM (when Marker models are loaded)
• GPU strongly recommended for PDF — CPU-only is extremely slow (~1 hour for a 27-page mixed PDF on i5 + 40GB RAM). A CUDA-capable GPU converts the same file in minutes.
• EPUB conversion is lightweight — no GPU needed, runs instantly

License

GPL-3.0