REPOGEO REPORT · LITE
sparkfish/augraphy
Default branch dev · commit ed4dcbda · scanned 6/11/2026, 1:17:59 AM
GitHub: 546 stars · 63 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 sparkfish/augraphy, 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.
- highreadme#1Reposition README opening to emphasize specialized document degradation for ML training
Why:
CURRENTAugraphy is a Python library that creates multiple copies of original documents though an augmentation pipeline that randomly distorts each copy -- degrading the clean version into dirty and realistic copies rendered through synthetic paper printing, faxing, scanning and copy machine processes.
COPY-PASTE FIXAugraphy is a specialized Python library for **generating synthetic training data** by applying **realistic document degradation effects**. It creates multiple copies of original documents through an augmentation pipeline that randomly distorts each copy, simulating real-world paper printing, faxing, scanning, and copy machine processes. Unlike generic image augmentation tools, Augraphy focuses specifically on manufacturing large volumes of high-quality noisy documents to train AI/ML models, particularly for tasks like OCR, where clean and noisy versions of target documents are scarce.
- mediumtopics#2Add more specific topics related to document degradation and OCR training
Why:
CURRENTaugmentation-pipeline, computer-vision, crappification, data-augmentation, data-pipeline, deep-neural-networks, image-processing, machine-learning, synthetic-data, synthetic-dataset-generation, training-data
COPY-PASTE FIXaugmentation-pipeline, computer-vision, crappification, data-augmentation, data-pipeline, deep-neural-networks, image-processing, machine-learning, synthetic-data, synthetic-dataset-generation, training-data, document-degradation, ocr-training, document-augmentation
- lowcomparison#3Add a 'Comparison' section to the README to differentiate from generic tools
Why:
COPY-PASTE FIX## Why Augraphy, not generic image augmentation? While libraries like OpenCV, Pillow, Augmentor, or Albumentations offer powerful general-purpose image transformations, Augraphy is uniquely designed for **realistic document degradation**. It simulates specific physical processes like printing, faxing, scanning, and copying, producing artifacts crucial for training robust AI/ML models on real-world document images, a capability not found in general-purpose tools.
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.
- OpenCV · recommended 2×
- Pillow · recommended 2×
- Augmentor · recommended 2×
- ImageMagick · recommended 2×
- SynthText · recommended 1×
- CATEGORY QUERYHow to generate synthetic training data with realistic document scanning and printing imperfections?you: not recommendedAI recommended (in order):
- SynthText
- Unreal Engine
- Unity
- OpenCV
- Pillow
- Pix2Pix
- CycleGAN
- Augmentor
- ImageMagick
AI recommended 9 alternatives but never named sparkfish/augraphy. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat tools can simulate real-world document degradation for computer vision model training?you: not recommendedAI recommended (in order):
- Augmentor
- OpenCV
- Pillow
- imgaug
- Albumentations
- Kornia
- ImageMagick
AI recommended 7 alternatives but never named sparkfish/augraphy. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
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 sparkfish/augraphy?passAI named sparkfish/augraphy explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts sparkfish/augraphy in production, what risks or prerequisites should they evaluate first?passAI named sparkfish/augraphy 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 sparkfish/augraphy solve, and who is the primary audience?passAI named sparkfish/augraphy explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
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sparkfish/augraphy — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
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- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite