OCR Super Surya

GPU-optimized OCR using Surya.

When to Use

• OCR, extract text from image, text recognition, 画像から文字
• Extracting text from screenshots, photos, or scanned images
• Processing PDFs with embedded images
• Multi-language document OCR (90+ languages including Japanese)

Features

Feature	Description
Accuracy	2x better than Tesseract (0.97 vs 0.88)
GPU	PyTorch-based, CUDA optimized
Languages	90+ including CJK
Layout	Document layout, table recognition

Quick Start

Installation

# 1. Check GPU
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"

# 2. Install (with CUDA if GPU available)
pip install surya-ocr

# If CUDA=False but you have GPU, reinstall PyTorch:
pip uninstall torch torchvision torchaudio -y
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Usage

# CLI
python scripts/ocr_helper.py image.png
python scripts/ocr_helper.py document.pdf -l ja en -o result.txt

# Or use surya directly
surya_ocr image.png --output_dir ./results

Python API

from PIL import Image
from surya.recognition import RecognitionPredictor
from surya.detection import DetectionPredictor
from surya.foundation import FoundationPredictor

image = Image.open("document.png")
found_pred = FoundationPredictor()
rec_pred = RecognitionPredictor(found_pred)
det_pred = DetectionPredictor()

for page in rec_pred([image], det_predictor=det_pred):
    for line in page.text_lines:
        print(line.text)

GPU Configuration

Variable	Default	Description
RECOGNITION_BATCH_SIZE	512	Reduce for lower VRAM
DETECTOR_BATCH_SIZE	36	Reduce if OOM

export RECOGNITION_BATCH_SIZE=256
surya_ocr image.png

Scripts

Script	Description
scripts/ocr_helper.py	Helper with OOM auto-retry, batch support

License Note

• Surya: GPL-3.0 (code), commercial license required for >$2M revenue