RRepoGEO

REPOGEO REPORT · LITE

getomni-ai/benchmark

Default branch main · commit cf84a584 · scanned 6/13/2026, 12:18:48 PM

GitHub: 633 stars · 53 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 getomni-ai/benchmark, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ocr, benchmark, llm, multimodal-llm, data-extraction, json-extraction, evaluation, gpt-4o, document-ai
  • highabout#2
    Update the repository's 'About' description

    Why:

    CURRENT
    OCR Benchmark
    COPY-PASTE FIX
    Benchmark for OCR and JSON data extraction accuracy of multimodal LLMs (e.g., GPT-4o) from documents.
  • mediumreadme#3
    Strengthen the README's opening sentence for clearer positioning

    Why:

    CURRENT
    A benchmarking tool that compares OCR and data extraction capabilities of different large multimodal models such as gpt-4o, evaluating both text and json extraction accuracy.
    COPY-PASTE FIX
    The Omni OCR Benchmark provides an open-source, comprehensive framework to evaluate and compare the OCR and JSON data extraction accuracy of large multimodal models (LMMs) like GPT-4o from documents. It helps AI researchers and engineers systematically assess LMM performance for critical document processing tasks.

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 getomni-ai/benchmark
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
heartexlabs/label-studio
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. heartexlabs/label-studio · recommended 1×
  2. doccano/doccano · recommended 1×
  3. jitsi/jiwer · recommended 1×
  4. Google Cloud Document AI · recommended 1×
  5. Amazon Textract · recommended 1×
  • CATEGORY QUERY
    How can I compare the accuracy of different OCR and document extraction models?
    you: not recommended
    AI recommended (in order):
    1. Label Studio (heartexlabs/label-studio)
    2. Doccano (doccano/doccano)
    3. jiwer (jitsi/jiwer)
    4. Google Cloud Document AI
    5. Amazon Textract
    6. Microsoft Azure Form Recognizer (now Azure AI Document Intelligence)
    7. Tesseract OCR (tesseract-ocr/tesseract)
    8. OpenCV (opencv/opencv)
    9. Abbyy FineReader Engine / Vantage
    10. Kofax OmniPage Capture SDK
    11. PaddleOCR (PaddlePaddle/PaddleOCR)

    AI recommended 11 alternatives but never named getomni-ai/benchmark. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help benchmark multimodal LLMs for JSON data extraction from documents?
    you: not recommended
    AI recommended (in order):
    1. Argilla (argilla-io/argilla)
    2. Ragas (explodinggradients/ragas)
    3. DeepEval (confident-ai/deepeval)
    4. LangChain Evaluation (langchain-ai/langchain)
    5. jsondiff (zoominfo/jsondiff)
    6. jsonpath_ng (h2non/jsonpath-ng)
    7. pydantic (pydantic/pydantic)
    8. Weights & Biases Prompts
    9. MLflow (mlflow/mlflow)

    AI recommended 9 alternatives but never named getomni-ai/benchmark. 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 getomni-ai/benchmark?
    pass
    AI named getomni-ai/benchmark explicitly

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

  • If a team adopts getomni-ai/benchmark in production, what risks or prerequisites should they evaluate first?
    pass
    AI named getomni-ai/benchmark 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 getomni-ai/benchmark solve, and who is the primary audience?
    pass
    AI did not name getomni-ai/benchmark — 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?

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getomni-ai/benchmark — 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