RRepoGEO

REPOGEO REPORT · LITE

Ucas-HaoranWei/GOT-OCR2.0

Default branch main · commit 179ed086 · scanned 6/29/2026, 9:57:48 AM

GitHub: 8,147 stars · 703 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Ucas-HaoranWei/GOT-OCR2.0, 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
  • highreadme#1
    Insert introductory paragraph clarifying 'GOT' after the main title

    Why:

    CURRENT
    <h3><a href="">General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model</a></h3>
    COPY-PASTE FIX
    This repository presents the official code implementation of **General OCR Theory (GOT)**, a unified end-to-end model for advanced general optical character recognition (OCR) tasks. Please note: 'GOT' refers to 'General OCR Theory' and is not associated with 'Game of Thrones'.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ["ocr", "optical-character-recognition", "deep-learning", "computer-vision", "end-to-end-ocr", "unified-model", "ai", "machine-learning"]
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a file named `LICENSE` in the root directory of the repository and populate it with the text of a standard open-source license, such as MIT, Apache-2.0, or GPL-3.0.

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 Ucas-HaoranWei/GOT-OCR2.0
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Vision AI · recommended 2×
  2. Amazon Textract · recommended 2×
  3. Microsoft Azure AI Vision · recommended 1×
  4. ABBYY FineReader Engine / Vantage · recommended 1×
  5. Tesseract OCR · recommended 1×
  • CATEGORY QUERY
    Looking for an advanced end-to-end optical character recognition solution.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Vision AI
    2. Amazon Textract
    3. Microsoft Azure AI Vision
    4. ABBYY FineReader Engine / Vantage
    5. Tesseract OCR
    6. OpenCV
    7. Kofax OmniPage Capture SDK

    AI recommended 7 alternatives but never named Ucas-HaoranWei/GOT-OCR2.0. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which unified models provide state-of-the-art performance for general OCR tasks?
    you: not recommended
    AI recommended (in order):
    1. PaddleOCR
    2. EasyOCR
    3. Google Cloud Vision AI
    4. Azure AI Vision
    5. Amazon Textract
    6. Keras-OCR
    7. Tesseract

    AI recommended 7 alternatives but never named Ucas-HaoranWei/GOT-OCR2.0. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Ucas-HaoranWei/GOT-OCR2.0?
    pass
    AI named Ucas-HaoranWei/GOT-OCR2.0 explicitly

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

  • If a team adopts Ucas-HaoranWei/GOT-OCR2.0 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Ucas-HaoranWei/GOT-OCR2.0 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 Ucas-HaoranWei/GOT-OCR2.0 solve, and who is the primary audience?
    pass
    AI named Ucas-HaoranWei/GOT-OCR2.0 explicitly

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

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Ucas-HaoranWei/GOT-OCR2.0 — 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