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)

Extraction and Verification

1. Confirm the input scope and a non-public output location for sensitive material; preserve source files and do not bypass document protection. Text redaction does not anonymize retained images or authorize redistribution.
2. Extract native PDF/slide text first, including slide notes where present. A nonempty text layer can still omit screenshots, diagrams, or image-only pages; inventory those gaps before selecting OCR targets. For image targets, resolve the highest-resolution original before recognizing: chat attachments and thumbnails are downscaled re-encodes, so locate the original capture by timestamp, then crop the region of interest and upscale 4-7x (LANCZOS). Dense CJK glyphs collapse at screen scale.
3. Record source-relative path, source hash, page/slide number, and image hash. Deduplicate identical images without dropping their source locations; distinguish pending, recognized, no-text, and failed outcomes.
4. Run a small sample and compare it with the original image before scaling. Reuse predictors within a batch and checkpoint each completed item so interruption does not require starting over.
5. Keep raw OCR separate from corrected notes. Flag low or missing confidence and inspect multi-column order, code symbols, and tables; high confidence is not proof of correctness, and no-text is not proof of an empty image. Confirm ambiguous CJK characters line by line on the zoomed crop and mark the unresolved ones; a plausible homoglyph is the default failure mode, not a blank.
6. Report extracted documents, processed OCR targets, unrecognized targets, and manually reviewed scope separately. Verify saved files and hashes; a saved hash alone proves neither source freshness nor semantic accuracy. Recheck the source hash when claiming the same source version.

Quick Start

Installation

Check the selected interpreter and existing dedicated environments before installing or declaring a PDF/OCR dependency unavailable. Set OcrPython to the resolved environment's interpreter; do not assume the active workspace environment has the same packages.

& $OcrPython -c "import importlib.util,json; print(json.dumps({name: importlib.util.find_spec(name) is not None for name in ['surya','torch','pypdf','pypdfium2']}))"

After imports succeed, check package versions and torch.cuda.is_available() with that same interpreter. GPU unavailability alone is not a reason to uninstall a working environment. If dependencies are missing, use an isolated environment; stop repeated equivalent TLS failures instead of disabling certificate validation.

Windows + uv 環境（OneDrive配下でのインストール）

OneDrive 配下のフォルダでは uv のハードリンクが失敗するため、以下の手順を使う：

# キャッシュをOneDrive外に設定
$env:UV_CACHE_DIR = "C:\Temp\uv_cache"

# 仮想環境をOneDrive外に作成
uv venv C:\Users\<USERNAME>\ocr_env --python 3.12

# surya-ocrをインストール（link-mode=copy でハードリンクを回避）
uv pip install surya-ocr --python C:\Users\<USERNAME>\ocr_env\Scripts\python.exe --link-mode=copy

# transformers 5.x は非互換 → 4.x を強制
uv pip install "transformers<5.0" --python C:\Users\<USERNAME>\ocr_env\Scripts\python.exe --link-mode=copy

Usage

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

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

Python API

import sys, io
# Windows CP932エンコードエラー対策
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')

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

image = Image.open("document.png").convert("RGB")
found_pred = FoundationPredictor()
rec_pred = RecognitionPredictor(found_pred)  # v0.13+ : FoundationPredictor必須
det_pred = DetectionPredictor()

# v0.17.x以降: langs引数は廃止 → 渡さないこと
for page in rec_pred([image], det_predictor=det_pred):
    for line in page.text_lines:
        if line.text.strip():
            print(line.text)

API変更履歴 (v0.17.x):

• RecognitionPredictor(foundation_predictor) - FoundationPredictor が必須引数に変更
• __call__() から langs 引数が削除（自動検出に変更）

GPU Configuration

export RECOGNITION_BATCH_SIZE=256
surya_ocr image.png

Scripts

Troubleshooting

License Note

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