vector-text-fixer

name	vector-text-fixer
description	Fix garbled text in PDF/SVG vector graphics caused by font encoding issues, making files editable in AI tools. Supports batch processing and JSON export for manual correction.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Vector Text Fixer

Fixes garbled text in PDF/SVG vector graphics caused by font embedding problems, encoding errors, or missing font substitution. Outputs repaired files or editable JSON for AI tool import.

Quick Check

python -m py_compile scripts/main.py

Audit-Ready Commands

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --input document.pdf --output fixed.pdf
python scripts/main.py --input diagram.svg --output fixed.svg

When to Use

• Fix garbled/box characters in PDF files caused by font embedding issues
• Repair SVG text encoding errors before editing in Illustrator or Inkscape
• Batch-process a folder of PDF/SVG files with garbled text
• Export a text map JSON for manual correction in AI editors

Workflow

1. Confirm input file path (PDF or SVG) or batch folder, and desired output path.
2. Validate that the request involves PDF/SVG garbled text repair; stop early if not.
3. Run scripts/main.py --input <file> --output <file> or --batch <folder>.
4. Return a structured result separating repaired blocks, skipped blocks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the Fallback Template below.

Fallback Template

If scripts/main.py fails or required fields are missing, respond with:

FALLBACK REPORT
───────────────────────────────────────
Objective        : <repair goal>
Inputs Available : <file path or batch folder provided>
Missing Inputs   : <list exactly what is missing>
  Note: --input requires a valid PDF or SVG file path, not a text string.
        For batch mode use --batch <folder_path> instead.
Partial Result   : <any blocks repaired safely>
Blocked Steps    : <what could not be completed and why>
Next Steps       : <minimum info needed to complete>
───────────────────────────────────────

Stress-Case Output Checklist

For complex multi-constraint requests, always include these sections explicitly:

• Assumptions: repair level default (standard), encoding auto-detected
• Constraints: encrypted PDFs require password unlock first; scanned PDFs need OCR first
• Risks: severely damaged files may not be fully repairable; rare fonts may not map correctly
• Unresolved Items: blocks with confidence < 0.3 flagged for manual review

Supported Scenarios

PDF Garbled Text:

• Box/question mark issues from font embedding problems
• Garbled text from encoding conversion errors
• Missing font substitution characters
• Multi-language mixed encoding issues

SVG Garbled Text:

• Text entity encoding errors
• Special character escaping issues
• Invalid font reference display abnormalities
• XML encoding declaration errors

CLI Usage

# Fix single PDF
python scripts/main.py --input document.pdf --output fixed.pdf

# Fix single SVG
python scripts/main.py --input diagram.svg --output fixed.svg

# Batch process folder
python scripts/main.py --batch ./input_folder --output ./output_folder

# Interactive repair
python scripts/main.py --input doc.pdf --interactive

# Export editable JSON
python scripts/main.py --input doc.pdf --export-json editable.json

# Specify repair level
python scripts/main.py --input doc.pdf --output fixed.pdf --repair-level aggressive

Parameters

Parameter	Required	Description	Default
--input	Yes*	Input PDF or SVG file path	—
--batch	Yes*	Batch input folder path	—
--output	Yes	Output file or folder path	—
--repair-level	No	minimal / standard / aggressive	standard
--interactive	No	Enable interactive repair mode	False
--export-json	No	Export editable JSON format	—
--encoding	No	Source file encoding (default: auto-detect)	auto

*At least one of --input or --batch is required.

Repair Levels

• Minimal: Only obvious errors (replacement characters, null bytes); maximum original integrity
• Standard: Common encoding issues + smart font replacement; balanced repair rate and accuracy
• Aggressive: Full text re-encoding + OCR-assisted recognition; for severely garbled documents

Output Format (JSON Export)

{
  "file_type": "pdf",
  "pages": [{
    "page_num": 1,
    "text_blocks": [{
      "id": "tb_001",
      "bbox": [100, 200, 300, 220],
      "original_text": "?????",
      "detected_encoding": "UTF-8",
      "confidence": 0.3,
      "suggested_fix": "Sample Text"
    }]
  }],
  "repair_summary": {
    "total_blocks": 15
     
     
  

Input Validation

This skill accepts: PDF (.pdf) or SVG (.svg) file paths, or a folder path for batch processing, where the files contain garbled or unreadable text caused by font/encoding issues.

If the request does not involve PDF/SVG garbled text repair — for example, asking to convert file formats, edit PDF content directly, perform OCR on scanned images, or process non-vector files — do not proceed. Instead respond:

"vector-text-fixer is designed to fix garbled text in PDF/SVG vector graphics caused by font encoding issues. Your request appears to be outside this scope. Please provide a valid PDF or SVG file path, or use a more appropriate tool."

Error Handling

• If --input receives a text string instead of a file path, report the error and request a valid file path.
• If the file is encrypted, report that password unlock is required before processing.
• If the task goes outside documented scope, stop instead of guessing.
• If scripts/main.py fails, use the Fallback Template above.
• Do not fabricate repaired text content or execution outcomes.

Output Requirements

Every final response must include:

1. Objective — file(s) repaired and repair level used
2. Inputs Received — file path, repair level, encoding settings
3. Assumptions — defaults applied (repair level, encoding detection)
4. Result — output file path, blocks fixed vs skipped
5. Risks and Limits — confidence thresholds, manual review blocks
6. Next Checks — review low-confidence blocks manually before use

Limitations

• Encrypted PDFs require password unlock before processing
• Severely damaged vector files may not be fully repairable
• Some rare fonts may not map correctly
• Scanned PDFs require OCR recognition first

Dependencies

pdfplumber >= 0.10.0
PyMuPDF >= 1.23.0
cairosvg >= 2.7.0
beautifulsoup4 >= 4.12.0
fonttools >= 4.40.0
chardet >= 5.0.0
Pillow >= 10.0.0