RRepoGEO

REPOGEO REPORT · LITE

xavctn/img2table

Default branch main · commit fba48730 · scanned 6/6/2026, 8:41:54 AM

GitHub: 870 stars · 119 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 xavctn/img2table, 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
    Reposition the README H1 and opening paragraph to highlight its unique value

    Why:

    CURRENT
    # img2table

`img2table` is a simple, easy to use, table identification and extraction Python Library based on OpenCV image processing that supports most common image file formats as well as PDF files.

Thanks to its design, it provides a practical and lighter alternative to Neural Networks based solutions, especially for usage on CPU.
    COPY-PASTE FIX
    # img2table: A CPU-Optimized Python Library for Table Extraction from Images and PDFs

`img2table` is a powerful and easy-to-use Python library designed for identifying and extracting structured table data from various image formats and PDF files. Leveraging OpenCV image processing, it offers a practical, lighter, and CPU-optimized alternative to heavy Neural Network-based solutions, making it ideal for local processing and environments where cloud services like Google Document AI or AWS Textract are not preferred.
  • hightopics#2
    Expand repository topics to include more specific keywords

    Why:

    CURRENT
    image-processing, opencv, python, table-extraction
    COPY-PASTE FIX
    table-extraction, document-processing, pdf-processing, ocr, python, opencv, image-processing, cpu-friendly
  • mediumhomepage#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add the project's official website or documentation link to the 'About' section.

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 xavctn/img2table
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Document AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Document AI · recommended 1×
  2. Azure AI Document Intelligence · recommended 1×
  3. Amazon Textract · recommended 1×
  4. tesseract-ocr/tesseract · recommended 1×
  5. opencv/opencv · recommended 1×
  • CATEGORY QUERY
    How to extract tabular data from scanned documents or image files using Python?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Document AI
    2. Azure AI Document Intelligence
    3. Amazon Textract
    4. Tesseract OCR (tesseract-ocr/tesseract)
    5. OpenCV (opencv/opencv)
    6. Camelot (camelot-dev/camelot)
    7. Tabula-py (tabulapdf/tabula-py)

    AI recommended 7 alternatives but never named xavctn/img2table. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good Python library for table extraction from images without heavy neural network dependencies?
    you: not recommended
    AI recommended (in order):
    1. OpenCV
    2. Tesseract OCR
    3. pytesseract
    4. Camelot
    5. Tabula-py
    6. ImageMagick
    7. Wand

    AI recommended 7 alternatives but never named xavctn/img2table. 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 xavctn/img2table?
    pass
    AI did not name xavctn/img2table — 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?

  • If a team adopts xavctn/img2table in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xavctn/img2table 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 xavctn/img2table solve, and who is the primary audience?
    pass
    AI named xavctn/img2table 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite