GPU-optimized OCR using Surya.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionocr-super-suryaExecute the skills CLI command in your project's root directory to begin installation:
Fetches ocr-super-surya from aktsmm/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ocr-super-surya. Access via /ocr-super-surya in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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GPU-optimized OCR using Surya.
| 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 |
# 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
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
# 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
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）
| 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
| Script | Description |
|---|---|
scripts/ocr_helper.py |
Helper with OOM auto-retry, batch support |
| エラー | 原因 | 対処 |
|---|---|---|
RecognitionPredictor.__init__() missing 1 required positional argument: 'foundation_predictor' |
v0.13+ でAPIが変更 | found_pred = FoundationPredictor() を作成して引数に渡す |
TypeError: __call__() got an unexpected keyword argument 'langs' |
v0.17.x で langs 引数廃止 |
langs 引数を削除する |
AttributeError: 'SuryaDecoderConfig' object has no attribute 'pad_token_id' |
transformers 5.x との非互換 |
pip install "transformers<5.0" でダウングレード |
failed to hardlink file ... OneDrive (uv, os error 396) |
OneDrive のハードリンク制限 | --link-mode=copy を付けてインストール＋UV_CACHE_DIR をOneDrive外に設定 |
UnicodeEncodeError: 'cp932' codec can't encode character |
Windows のCP932デフォルトエンコード | sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') を先頭に追加 |
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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ocr-super-surya fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added ocr-super-surya from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
ocr-super-surya has been reliable in day-to-day use. Documentation quality is above average for community skills.
ocr-super-surya reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for ocr-super-surya matched our evaluation — installs cleanly and behaves as described in the markdown.
ocr-super-surya fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ocr-super-surya has been reliable in day-to-day use. Documentation quality is above average for community skills.
ocr-super-surya reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: ocr-super-surya is focused, and the summary matches what you get after install.
Registry listing for ocr-super-surya matched our evaluation — installs cleanly and behaves as described in the markdown.
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