Vector Text Fixer

Fixes garbled text in PDF/SVG vector graphics to make them editable in AI tools.

Features

• - Garbled Text Detection: Automatically identifies garbled text in PDF/SVG files
• Smart Repair: Infers original text content based on context
• Batch Processing: Supports batch processing of multiple files in a folder
• Format Preservation: Repaired files maintain original vector format and layout
• AI-assisted Editing: Outputs intermediate format that can be imported into AI editors

Supported Scenarios

1. PDF Garbled Text Repair

• - Box/question mark issues caused by font embedding problems
• Garbled text caused by encoding conversion errors
• Abnormal characters generated by missing font substitution
• Multi-language mixed encoding issues

2. SVG Garbled Text Repair

• - Text entity encoding errors
• Special character escaping issues
• Display abnormalities caused by invalid font references
• XML encoding declaration errors

Usage

Command Line

CODEBLOCK0

Python API

CODEBLOCK1

Input Parameters

*At least one of --input and --batch is required

Output Format

Repaired PDF/SVG

• - Maintains original vector format
• Garbled text replaced with readable content
• Fonts and layout remain unchanged

JSON Export Format

Garbled Text Detection Rules

The tool uses the following rules to detect garbled text:

1. 1. Replacement Character Detection: Identifies U+FFFD (�) and box characters
2. Control Character Filtering: Excludes non-printing control characters
3. Encoding Consistency: Detects anomalies caused by mixed encodings
4. Font Fallback Detection: Identifies substitution characters generated due to missing fonts
5. Probability Model: Garbled text probability assessment based on character frequency

Repair Strategies

Minimal

• - Only repairs obvious errors (replacement characters, null bytes)
• Maintains maximum integrity of original text
• Suitable for minor garbled text issues

Standard

• - Repairs common encoding issues
• Smart font replacement
• Balances repair rate and accuracy

Aggressive

• - Comprehensive text re-encoding
• Uses OCR-assisted recognition
• Suitable for severely garbled documents

Examples

Fix Single Page PDF

Input:
CODEBLOCK3

Output:
CODEBLOCK4

Export Editable JSON

Input:
CODEBLOCK5

Output JSON Structure:
CODEBLOCK6

Dependencies

CODEBLOCK7

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

Version Information

• - Version: 1.0.0
• Last Updated: 2026-02-06
• Status: Ready for use

Risk Assessment

Security Checklist

• - [ ] No hardcoded credentials or API keys
• [ ] No unauthorized file system access (../)
• [ ] Output does not expose sensitive information
• [ ] Prompt injection protections in place
• [ ] Input file paths validated (no ../ traversal)
• [ ] Output directory restricted to workspace
• [ ] Script execution in sandboxed environment
• [ ] Error messages sanitized (no stack traces exposed)
• [ ] Dependencies audited

Prerequisites

CODEBLOCK8

Evaluation Criteria

Success Metrics

• - [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. 1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• - Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements: