RRepoGEO

REPOGEO REPORT · LITE

Yuliang-Liu/MonkeyOCR

Default branch main · commit 63b2a626 · scanned 6/29/2026, 12:37:54 PM

GitHub: 6,601 stars · 458 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
3 / 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 Yuliang-Liu/MonkeyOCR, 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

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

OVERALL DIRECTION
  • hightopics#1
    Add specific topics for document parsing and LMMs

    Why:

    COPY-PASTE FIX
    document-parsing, lmm, ocr, multimodal, document-intelligence, layout-analysis, deep-learning, computer-vision
  • highreadme#2
    Add a concise, keyword-rich opening sentence to the README

    Why:

    COPY-PASTE FIX
    MonkeyOCR is a lightweight, LMM-based document parsing model designed for robust extraction of structured information from diverse document layouts, leveraging a Structure-Recognition-Relation Triplet Paradigm.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://huggingface.co/echo840/MonkeyOCR

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 Yuliang-Liu/MonkeyOCR
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure AI Document Intelligence
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure AI Document Intelligence · recommended 1×
  2. Google Cloud Document AI · recommended 1×
  3. AWS Textract · recommended 1×
  4. OpenAI GPT-4 · recommended 1×
  5. PyMuPDF (fitz) · recommended 1×
  • CATEGORY QUERY
    How to extract structured information from complex PDF documents using an LMM?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Document Intelligence
    2. Google Cloud Document AI
    3. AWS Textract
    4. OpenAI GPT-4
    5. PyMuPDF (fitz)
    6. pdfminer.six
    7. unstructured.io
    8. LlamaIndex
    9. LangChain
    10. Nanonets
    11. DocuMotor

    AI recommended 11 alternatives but never named Yuliang-Liu/MonkeyOCR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a lightweight model for robust parsing of diverse document layouts and structures.
    you: not recommended
    AI recommended (in order):
    1. LayoutParser (layout-parser/layout-parser)
    2. PaddleOCR (PaddlePaddle/PaddleOCR)
    3. DocTR (mindee/doctr)
    4. DeepDoctection (deepdoctection/deepdoctection)
    5. Tesseract (tesseract-ocr/tesseract)

    AI recommended 5 alternatives but never named Yuliang-Liu/MonkeyOCR. 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 Yuliang-Liu/MonkeyOCR?
    pass
    AI named Yuliang-Liu/MonkeyOCR explicitly

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

  • If a team adopts Yuliang-Liu/MonkeyOCR in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Yuliang-Liu/MonkeyOCR 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 Yuliang-Liu/MonkeyOCR solve, and who is the primary audience?
    pass
    AI named Yuliang-Liu/MonkeyOCR explicitly

    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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MARKDOWN (README)
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Yuliang-Liu/MonkeyOCR — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite