REPOGEO REPORT · LITE

NanoNets/docext

Default branch main · commit 8a08bbd5 · scanned 6/29/2026, 7:11:58 AM

GitHub: 2,027 stars · 147 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

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 NanoNets/docext, 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

Reconcile 'OCR-free' claim with 'Nanonets-OCR-s' model name in README

Why:

CURRENT

New Model Release: Nanonets-OCR-s ... specifically trained for efficient image to markdown conversion with semantic understanding for images, signatures, watermarks, etc.!

COPY-PASTE FIX

New Model Release: Nanonets-OCR-s, a compact 3B parameter vision-language model (VLM) that performs *OCR-free* image to markdown conversion with semantic understanding, *replacing* traditional OCR for document intelligence tasks!

highreadme#2

Reposition README opening to highlight OCR-free VLM and on-premises nature

Why:

CURRENT

<p align="center"><em>An on-premises document information extraction and benchmarking toolkit.</em></p>

COPY-PASTE FIX

<p align="center"><em>An on-premises, OCR-free unstructured data extraction, markdown conversion, and benchmarking toolkit powered by vision-language models (VLMs).</em></p>

mediumtopics#3

Remove misleading 'OCR' related topics that contradict 'OCR-free' positioning

Why:

CURRENT

document, document-analysis, document-data-extraction, document-information-extraction, extraction, llm-ocr, llms, machine-learning, nlp, ocr, ocr-benchmark, ocr-onpremise, onprem, onprem-ocr, onprem-vision, onpremise, rag, table-extraction, unstructured-data, vlms

COPY-PASTE FIX

document, document-analysis, document-data-extraction, document-information-extraction, extraction, llms, machine-learning, nlp, onprem, onprem-vision, onpremise, rag, table-extraction, unstructured-data, vlms, vision-language-models, document-intelligence, markdown-conversion, data-extraction-benchmark

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 NanoNets/docext

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Apache Tika

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Apache Tika · recommended 2×
Google Cloud Document AI · recommended 2×
spaCy · recommended 1×
OpenNLP · recommended 1×
NLTK · recommended 1×

CATEGORY QUERY
How to perform on-premises unstructured document data extraction without traditional OCR?
you: not recommended
AI recommended (in order):
1. Apache Tika
2. spaCy
3. OpenNLP
4. NLTK
5. Microsoft Form Recognizer
6. Google Cloud Document AI
AI recommended 6 alternatives but never named NanoNets/docext. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Toolkit for converting document images to structured markdown and benchmarking extraction accuracy?
you: not recommended
AI recommended (in order):
1. LayoutParser
2. Apache Tika
3. Pandoc
4. markdown-it-py
5. mistune
6. Google Cloud Document AI
7. Azure AI Document Intelligence
8. Amazon Textract
9. OpenCV
10. Tesseract OCR
11. marked
AI recommended 11 alternatives but never named NanoNets/docext. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 NanoNets/docext?
pass
AI named NanoNets/docext explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts NanoNets/docext in production, what risks or prerequisites should they evaluate first?
pass
AI named NanoNets/docext 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 NanoNets/docext solve, and who is the primary audience?
pass
AI named NanoNets/docext explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of NanoNets/docext. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/NanoNets/docext"><img src="https://repogeo.com/badge/NanoNets/docext.svg" alt="RepoGEO" /></a>

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