Fork notice: This is harissaninja/Manazir-OCR, a fork of h9-tec/Manazir-OCR patched for API-only installs. The original requires
torch,torchvision,transformers, andaccelerateas core dependencies (~3-5GB). This fork moves those to an optional[local-models]extra so you can install the CLI + remote API backends (vLLM, Mistral OCR, OpenAI GPT-4o) without the ML stack.pip install git+https://github.com/harissaninja/Manazir-OCR.gitThat's it — no clone, no venv step needed. The
manazirCLI is ready to use after this single command.
- API-only:
pip install git+https://github.com/harissaninja/Manazir-OCR.git- Full local models:
pip install git+https://github.com/harissaninja/Manazir-OCR.git#egg=manazir-ocr[local-models]
Manazir OCR is an Arabic‑first, layout‑aware OCR framework inspired by Ibn al‑Haytham’s Kitāb al‑Manāẓir (Book of Optics). It orchestrates multiple backends (local Transformers, vLLM servers, lightweight engines, and commercial APIs) to extract high‑quality text from PDFs and images, producing Markdown/HTML with layout blocks and figure crops.
- Arabic‑first multi‑model architecture: choose or auto‑route among VLMs and classic OCR.
- Layout‑aware outputs: HTML/Markdown with reading order, chunk metadata, and image extraction.
- Flexible runtimes: local Hugging Face (Transformers) or remote vLLM; pluggable registry + factory.
- CLI and Streamlit UIs: production CLI and two apps (basic + professional, with an optional Nerd theme).
- Extensible: add models via a simple registry entry; commercial APIs optional.
Install from source (recommended):
python -m venv venv
source venv/bin/activate # on Windows: venv\Scripts\activate
pip install -U pip
pip install -e .Run the CLI:
# Batch processing with layout-aware pipeline (vLLM or HF)
manazir process <input_path> <output_dir> --method vllm --paginate_output
# Arabic-first single-file OCR with model selection
manazir ocr file.pdf --language ar --quality highest --device cuda
# Browse models and get recommendations
manazir list-available-models
manazir recommend --language ar --document-type handwrittenLaunch the apps:
manazir_app # Basic demo (choose Classic hf/vllm or pick a Registry model)
manazir_app_pro # Professional UI (toggle Nerd Theme in sidebar)- Run the Professional app (recommended):
manazir_app_pro
# then open http://localhost:8501Alternative:
python -m docustruct.scripts.run_app_professional- Run via CLI:
# Convert a PDF to Markdown using Arabic-first defaults
manazir ocr input.pdf --language ar --output out_dir
# List models and pick one
manazir list-available-models
manazir ocr input.png --language ar --model qwen2_vl_2b- Specialized:
qari_ocr,dimi_arabic_ocr,ocr_rl2,trocr_arabic - Multilingual VLMs:
qwen2_vl_2b,qwen2_5_vl_7b_arabic - Lightweight:
paddle_ocr_arabic,paddle_ocr_arabic_v4,tesseract,easy_ocr - Layout toolkit:
surya_ocr,surya_ocr_arabic - Commercial (optional):
openai_gpt4o,mistral_ocr
Additions in this repo: qwen2_5_vl_7b_arabic, paddle_ocr_arabic_v4, surya_ocr_arabic, qari_ocr_waraqon.
from docustruct.model import create_model
from PIL import Image
model = create_model("qwen2_5_vl_7b_arabic", device="cuda")
img = Image.open("page.png").convert("RGB")
result = model.process_image(img)
print(result.text)The CLI’s vLLM path uses OpenAI‑compatible endpoints. Configure via env:
export VLLM_API_KEY=EMPTY
export VLLM_API_BASE=http://localhost:8000/v1
export VLLM_MODEL_NAME=manazir- Import path remains
docustruct.*for now; package name ismanazir-ocr. - Some backends require extra installs (e.g.,
paddleocr). Commercial APIs need keys and may incur costs.
- Store project screenshots and images in
assets/. - Included:
assets/screenshot-ui.png,assets/screenshot-available-models.png.
Original development by Hesham Haroon — contact: heshamharoon19@gmail.com. API-only fork by harissaninja — see github.com/harissaninja/Manazir-OCR.
Code is Apache‑2.0. Some integrated models (e.g., Surya) are GPL‑3.0; verify licenses before use.