ocrxdoc

ocrxdoc - Python Framework for OCR

A clean, easy-to-use Python framework for OCR (Optical Character Recognition) using Qwen3-VL AI models. Supports images (JPG, PNG, JPEG), PDF, DOCX, and TXT files.

Features

• 🖼️ Image OCR: Support for JPG, PNG, JPEG
• 📄 Document OCR: Support for PDF, DOCX, TXT
• 🤖 Two AI Models:
  • 4B model (default) - More accurate
  • 2B model - Faster
• 🖥️ GPU/CPU Support: Automatic GPU detection and usage
• 🎯 ROI Selection: Select custom regions for OCR
• 📦 Batch Processing: Process multiple files at once
• ⚡ Easy to Use: Simple, clean API

Installation

Basic Installation

pip install ocrxdoc

With PDF Support

pip install ocrxdoc[pdf]

With DOCX Support

pip install ocrxdoc[docx]

With All Features

pip install ocrxdoc[all]

Quick Start

Basic Usage

from ocrxdoc import OCREngine

# Initialize OCR engine
engine = OCREngine(model_size="4B", device="auto")

# Load model
engine.load_model()

# Process an image
result = engine.ocr("path/to/image.jpg", prompt="Extract all text from this image")
print(result)

Process Different File Types

from ocrxdoc import OCREngine

engine = OCREngine(model_size="4B")
engine.load_model()

# Process image
result = engine.ocr("image.jpg")

# Process PDF
result = engine.ocr("document.pdf")

# Process DOCX
result = engine.ocr("document.docx")

# Process TXT
result = engine.ocr("text.txt")

Batch Processing

from ocrxdoc import OCREngine

engine = OCREngine(model_size="4B")
engine.load_model()

files = ["image1.jpg", "image2.png", "document.pdf"]

def progress_callback(current, total, filename):
    print(f"Processing {current}/{total}: {filename}")

results = engine.ocr_batch(files, progress_callback=progress_callback)

for file_path, result in results:
    print(f"{file_path}: {result[:100]}...")

Custom Model Path

from ocrxdoc import OCREngine

# Use custom model path
engine = OCREngine(
    model_path="./custom/models/Qwen3-VL-4B-Instruct",
    device="cuda:0"
)
engine.load_model()

ROI (Region of Interest) Selection

from ocrxdoc import OCREngine

engine = OCREngine(model_size="4B")
engine.load_model()

# OCR only a specific region: (x, y, width, height)
result = engine.ocr(
    "image.jpg",
    roi=(100, 100, 500, 300)  # Crop region before OCR
)

Custom Generation Parameters

from ocrxdoc import OCREngine

engine = OCREngine(
    model_size="4B",
    max_tokens=5000,
    temperature=0.1,
    top_p=0.9
)
engine.load_model()

# Or update after initialization
engine.set_generation_params(
    max_tokens=5000,
    temperature=0.1
)

Model Setup

Models need to be downloaded manually due to their large size:

• 4B Model (Default):

  • Download from: Hugging Face - Qwen3-VL-4B-Instruct
  • Place in: ./models/Qwen3-VL-4B-Instruct/

• 2B Model:

  • Download from: Hugging Face - Qwen3-VL-2B-Instruct
  • Place in: ./models/Qwen3-VL-2B-Instruct/

Requirements

• Python 3.8+
• PyTorch 2.0+
• Transformers 4.57+
• Pillow 10.0+
• For PDF: pdf2image and Poppler
• For DOCX: python-docx

System Requirements

• RAM: Minimum 16GB (recommended 32GB+)
• GPU: Recommended (NVIDIA with CUDA support) - VRAM minimum 8GB
• Paging File: Minimum 8GB for 4B model, 4GB for 2B model

API Reference

OCREngine

Main OCR engine class.

__init__(model_path=None, model_size="4B", device="auto", dtype=None, poppler_path=None, max_tokens=3000, temperature=0.2, top_p=0.8, top_k=50, repetition_penalty=1.1)

Initialize OCR engine.

load_model()

Load the OCR model and processor.

ocr(file_path, prompt="...", roi=None)

Perform OCR on a file.

• file_path: Path to file
• prompt: Prompt for OCR model
• roi: Optional region of interest as (x, y, width, height)

Returns: Extracted text string

ocr_batch(file_paths, prompt="...", progress_callback=None)

Perform OCR on multiple files.

• file_paths: List of file paths
• prompt: Prompt for OCR model
• progress_callback: Optional callback(current, total, filename)

Returns: List of tuples (file_path, ocr_result)

set_generation_params(max_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None)

Update generation parameters.

cleanup()

Clean up temporary files.

Examples

See examples/ directory for more examples.

License

MIT License

Acknowledgments

• Qwen3-VL - AI Model
• Hugging Face Transformers
• PyTorch