ocr-super-surya
GPU-optimized OCR using Surya.
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Installation Guide
How to use ocr-super-surya on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
ocr-super-surya
Run the install command
Execute 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.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
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.
Security Notice
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.
Documentation
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
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 -l ja en -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
| 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 |
Troubleshooting
| 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 |
License Note
- Surya: GPL-3.0 (code), commercial license required for >$2M revenue
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ 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.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- NNoor Abebe★★★★★Dec 28, 2024
ocr-super-surya fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- MMichael Desai★★★★★Dec 16, 2024
We added ocr-super-surya from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- CChen Choi★★★★★Dec 4, 2024
ocr-super-surya has been reliable in day-to-day use. Documentation quality is above average for community skills.
- LLiam Kim★★★★★Nov 27, 2024
ocr-super-surya reduced setup friction for our internal harness; good balance of opinion and flexibility.
- RRahul Santra★★★★★Nov 23, 2024
Registry listing for ocr-super-surya matched our evaluation — installs cleanly and behaves as described in the markdown.
- SSofia Reddy★★★★★Nov 23, 2024
ocr-super-surya fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- VValentina Torres★★★★★Nov 19, 2024
ocr-super-surya has been reliable in day-to-day use. Documentation quality is above average for community skills.
- IIshan Harris★★★★★Nov 7, 2024
ocr-super-surya reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAma Malhotra★★★★★Nov 7, 2024
Solid pick for teams standardizing on skills: ocr-super-surya is focused, and the summary matches what you get after install.
- AArya Bhatia★★★★★Nov 3, 2024
Registry listing for ocr-super-surya matched our evaluation — installs cleanly and behaves as described in the markdown.
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