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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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[ "MD", "User Content", "Technical", "Correspondence" ]
BLOOM EFFECT.md
js for projectiles (non wireframe for neurots
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js for projectiles (non wireframe for neurots
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BLOOM EFFECT.md
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[ "MD", "User Content", "Correspondence", "Long Text" ]
BLOOM EFFECT.md
Vector2(window
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BLOOM EFFECT.md
innerWidth, window
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BLOOM EFFECT.md
innerHeight), 0
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BLOOM EFFECT.md
8, 0, 0) bloomPass
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8, 0, 0) bloomPass
[ "MD", "User Content", "Short Text" ]
BLOOM EFFECT.md
renderToScreen = true const bloomComposer = new EffectComposer(renderer) bloomComposer
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renderToScreen = true const bloomComposer = new EffectComposer(renderer) bloomComposer
[ "MD", "User Content" ]
BLOOM EFFECT.md
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BLOOM EFFECT.md
innerWidth, window
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BLOOM EFFECT.md
innerHeight) bloomComposer
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BLOOM EFFECT.md
addPass(renderScene) bloomComposer
add[ass(rwnd&;eRIS&1enE) b+lpomvompos1e}
addPass(renderScene) bloomComposer
[ "MD", "User Content", "Short Text" ]
BLOOM EFFECT.md
addPass(bloomPass) //GLITCH const glitchPass = new GlitchPass() glitchPass
add|Pass!(;nloOn[as$s) //g^LoTCg vi;nsr ;gliychpas1s = bew1 hlll[txg|}asa() gLItch@ss!s
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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[ "MD", "User Content" ]
BLOOM EFFECT.md
setSize(window
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BLOOM EFFECT.md
innerWidth, window
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BLOOM EFFECT.md
innerHeight) glitchComposer
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[ "MD", "User Content", "Short Text" ]
BLOOM EFFECT.md
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[ "MD", "User Content", "Short Text" ]
BLOOM EFFECT.md
addPass(bloomPass) glitchComposer
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addPass(bloomPass) glitchComposer
[ "MD", "User Content", "Short Text" ]
BLOOM EFFECT.md
addPass(glitchPass) const tick = () => { controls
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[ "MD", "User Content", "Short Text" ]
BLOOM EFFECT.md
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[ "MD", "User Content", "Short Text" ]
BLOOM EFFECT.md
autoClear = false renderer
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autoClear = false renderer
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BLOOM EFFECT.md
      ___           ___                         ___            ___           ___     
     /\  \         /\__\         _____         /|  |          /\  \         /\__\    
    _\:\  \       /:/ _/_       /::\  \       |:|  |         /::\  \       /:/ _/_   
   /\ \:\  \     /:/ /\__\     /:/\:\  \      |:|  |        /:/\:\  \     /:/ /\  \  
  _\:\ \:\  \   /:/ /:/ _/_   /:/ /::\__\   __|:|__|       /:/  \:\  \   /:/ /::\  \ 
 /\ \:\ \:\__\ /:/_/:/ /\__\ /:/_/:/\:|__| /::::\__\_____ /:/__/ \:\__\ /:/_/:/\:\__\
 \:\ \:\/:/  / \:\/:/ /:/  / \:\/:/ /:/  / ~~~~\::::/___/ \:\  \ /:/  / \:\/:/ /:/  /
  \:\ \::/  /   \::/_/:/  /   \::/_/:/  /      |:|~~|      \:\  /:/  /   \::/ /:/  / 
   \:\/:/  /     \:\/:/  /     \:\/:/  /       |:|  |       \:\/:/  /     \/_/:/  /  
    \::/  /       \::/  /       \::/  /        |:|__|        \::/  /        /:/  /   
     \/__/         \/__/         \/__/         |/__/          \/__/         \/__/    
    

Synthetic Error Correction

UNDER DEVELOPMENT

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

Supported Tasks and Leaderboards

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

Languages

English

Dataset Structure

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

Dataset Creation

Curation Rationale

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

Source Data

The data was generated from user-uploaded documents in .txt, .csv, .md, or .jsonl formats.

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.

Considerations for Using the Data

Social Impact of Dataset

This dataset enables the creation of error correction models for various text domains.

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)

Additional Information

Dataset Curators

Generated by TYPE/RIGHTER PWA by webXOS

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}
}

Contributions

Thanks to the user who generated this dataset using TYPE/RIGHTER.

Error Distribution

  • Keyboard Adjacent Errors: 20%
  • Case Corruption: 15%
  • Symbol Insertion: 10%
  • OCR Line Noise: 10%

Generated on

2026-01-07T01:04:58.601Z


Generated by TYPE/RIGHTER • Offline-First PWA • Privacy by Design • webXOS 2026

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