RRepoGEO

REPOGEO REPORT · LITE

Nutlope/llama-ocr

Default branch main · commit 1b588159 · scanned 5/11/2026, 2:52:55 PM

GitHub: 2,427 stars · 237 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Nutlope/llama-ocr, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0) in the repository root to clearly state the project's licensing.
  • highreadme#2
    Strengthen README opening to highlight LLM-based OCR differentiation

    Why:

    CURRENT
    <h1 align="center">Llama OCR</h1>
	<p>An npm library to run OCR for free with Llama 3.2 Vision.</p>
    COPY-PASTE FIX
    <h1 align="center">Llama OCR: Document to Markdown with Llama 3.2 Vision</h1>
	<p>A Node.js library that leverages Llama 3.2 Vision to perform advanced Optical Character Recognition (OCR), transforming scanned documents and images directly into structured markdown text. Unlike traditional OCR, Llama OCR understands context to provide richer, more usable output.</p>

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Nutlope/llama-ocr
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Vision AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Vision AI · recommended 1×
  2. Amazon Textract · recommended 1×
  3. Microsoft Azure Form Recognizer · recommended 1×
  4. Tesseract OCR · recommended 1×
  5. pytesseract · recommended 1×
  • CATEGORY QUERY
    How can I programmatically convert scanned document images into structured markdown text?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Vision AI
    2. Amazon Textract
    3. Microsoft Azure Form Recognizer
    4. Tesseract OCR
    5. pytesseract
    6. pdfminer.six
    7. layoutparser
    8. OpenCV

    AI recommended 8 alternatives but never named Nutlope/llama-ocr. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Node.js library to perform advanced optical character recognition on images using vision models.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Vision API (googleapis/nodejs-vision)
    2. AWS Rekognition (aws/aws-sdk-js-v3)
    3. Microsoft Azure Cognitive Services - Computer Vision (Azure/azure-sdk-for-js)
    4. Tesseract.js (naptha/tesseract.js)
    5. OpenCV.js (opencv/opencv.js)
    6. PaddleOCR (PaddlePaddle/PaddleOCR)

    AI recommended 6 alternatives but never named Nutlope/llama-ocr. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Nutlope/llama-ocr?
    pass
    AI named Nutlope/llama-ocr explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Nutlope/llama-ocr in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Nutlope/llama-ocr explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Nutlope/llama-ocr solve, and who is the primary audience?
    pass
    AI did not name Nutlope/llama-ocr — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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