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**USE THIS FOR THE SCENE RENDERING FOR A MID 90's 3D GLITCH BLOOM EFFECT TO EMULATED A LEGACY RENDERING SYSTEM (THINK 80s 90s demoscene 3d scene json design using three js wireframes and neurot
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BLOOM EFFECT.md
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js for projectiles (non wireframe for neurots
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
renderToScreen = true const bloomComposer = new EffectComposer(renderer) bloomComposer
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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addPass(renderScene) bloomComposer
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BLOOM EFFECT.md
addPass(bloomPass) //GLITCH const glitchPass = new GlitchPass() glitchPass
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BLOOM EFFECT.md
renderToScreen = true const glitchComposer = new EffectComposer(renderer) glitchComposer
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md
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BLOOM EFFECT.md

Website GitHub Hugging Face Follow on X

      ___           ___                         ___            ___           ___     
     /\  \         /\__\         _____         /|  |          /\  \         /\__\    
    _\:\  \       /:/ _/_       /::\  \       |:|  |         /::\  \       /:/ _/_   
   /\ \:\  \     /:/ /\__\     /:/\:\  \      |:|  |        /:/\:\  \     /:/ /\  \  
  _\:\ \:\  \   /:/ /:/ _/_   /:/ /::\__\   __|:|__|       /:/  \:\  \   /:/ /::\  \ 
 /\ \:\ \:\__\ /:/_/:/ /\__\ /:/_/:/\:|__| /::::\__\_____ /:/__/ \:\__\ /:/_/:/\:\__\
 \:\ \:\/:/  / \:\/:/ /:/  / \:\/:/ /:/  / ~~~~\::::/___/ \:\  \ /:/  / \:\/:/ /:/  /
  \:\ \::/  /   \::/_/:/  /   \::/_/:/  /      |:|~~|      \:\  /:/  /   \::/ /:/  / 
   \:\/:/  /     \:\/:/  /     \:\/:/  /       |:|  |       \:\/:/  /     \/_/:/  /  
    \::/  /       \::/  /       \::/  /        |:|__|        \::/  /        /:/  /   
     \/__/         \/__/         \/__/         |/__/          \/__/         \/__/    
    

Synthetic Error Correction

Small synthetic dataset (28 examples: 22 train, 6 test) of error-correction pairs for training text correction models (e.g., OCR post-processing, typos).

Dataset Summary

This dataset was generated locally using the TYPE/RIGHTER offline-first PWA. It contains synthetic error-correction pairs for training text correction models.

Generated by TYPE/RIGHTER • Offline-First PWA, downloadable in the /typerighter/ folder of the repo so users can make their own similar datasets

Supported Tasks and Leaderboards

  • Text error correction
  • OCR post-processing
  • Typographical error correction

Data Instances

Each instance contains:

  • original: The text as it appeared in the uploaded file
  • corrupted: The procedurally "damaged" version for error detection training
  • target: The cleaned, normalized version for error correction training
  • tags: Content-based classification tags
  • source: Original file name

Data Fields

  • original: string - Original text
  • corrupted: string - Text with injected errors
  • target: string - Cleaned target text
  • tags: Sequence[string] - Content classification tags
  • source: string - Source file name

Data Splits

  • Train: 22 examples
  • Test: 6 examples

Curation Rationale

This dataset was created to provide synthetic data for training error correction models from user-provided documents.

Annotations

Annotations were automatically generated through:

  1. Text normalization (simulated cleaning)
  2. Controlled error injection based on configurable parameters
  3. Content-based classification

Personal and Sensitive Information

All processing occurs locally in the browser. No data was uploaded to any server.

Discussion of Biases

The error patterns are synthetic and may not perfectly represent real-world error distributions.

Other Known Limitations

  • Synthetic errors may not capture all real-world error patterns
  • Limited to text-based errors (no formatting or layout errors)

Licensing Information

MIT

Citation Information

@misc{typerighter2026,
  title={TYPE/RIGHTER Synthetic Error Correction Dataset},
  author={TYPE/RIGHTER PWA},
  year={2026},
  url={https://github.com/typerighter}
}
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