ocr-super-surya

aktsmm/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/aktsmm/agent-skills --skill ocr-super-surya
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summary

GPU-optimized OCR using Surya.

skill.md

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
how to use ocr-super-surya

How to use ocr-super-surya on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add ocr-super-surya
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aktsmm/agent-skills --skill ocr-super-surya

The skills CLI fetches ocr-super-surya from GitHub repository aktsmm/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/ocr-super-surya

Reload or restart Cursor to activate ocr-super-surya. Access the skill through slash commands (e.g., /ocr-super-surya) or your agent's skill management interface.

Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.769 reviews
  • Noor Abebe· Dec 28, 2024

    ocr-super-surya fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Michael Desai· Dec 16, 2024

    We added ocr-super-surya from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chen Choi· Dec 4, 2024

    ocr-super-surya has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Liam Kim· Nov 27, 2024

    ocr-super-surya reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Rahul Santra· Nov 23, 2024

    Registry listing for ocr-super-surya matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Sofia Reddy· Nov 23, 2024

    ocr-super-surya fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Valentina Torres· Nov 19, 2024

    ocr-super-surya has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ishan Harris· Nov 7, 2024

    ocr-super-surya reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ama 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.

  • Arya 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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