133 lines
4.3 KiB
Markdown
133 lines
4.3 KiB
Markdown
# ComfyUI String Similarity Node
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A comprehensive text comparison node for ComfyUI that provides multiple algorithms to measure string similarity, useful for OCR validation, text comparison, and quality assessment workflows.
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## Features
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- Multiple similarity algorithms in one node
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- Detailed comparison metrics
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- Support for both character-level and semantic similarity
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- Useful for OCR accuracy assessment
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- Compatible with ComfyUI's text processing pipeline
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## Supported Algorithms
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1. **Levenshtein Distance**: Edit distance between strings
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2. **SequenceMatcher**: Python's built-in sequence matching
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3. **Jaccard Similarity**: Set-based word comparison
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4. **Cosine Similarity**: Vector space model comparison
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5. **Word Error Rate (WER)**: Word-level accuracy metric
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6. **Character Error Rate (CER)**: Character-level accuracy metric
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7. **SentenceTransformer-MPNET**: Deep learning semantic similarity
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8. **SentenceTransformer-MiniLM**: Lightweight semantic similarity
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## Installation
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1. Clone this repository into your ComfyUI custom_nodes folder:
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```bash
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cd ComfyUI/custom_nodes
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git clone https://github.com/yourusername/ComfyUI-String-Similarity
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```
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Restart ComfyUI
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## Usage
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1. Add the "String Similarity Node" to your workflow
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2. Connect two text inputs (actual_text and ocr_text)
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3. Select the comparison algorithm
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4. The node outputs a formatted string with similarity metrics
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### Input Parameters
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- **actual_text**: The reference or ground truth text
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- **ocr_text**: The text to compare (e.g., OCR output)
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- **algorithm**: Choose from 8 different similarity algorithms
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### Output
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- **STRING**: Formatted result showing the similarity score and additional metrics
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## Algorithm Details
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### Distance-Based Metrics
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- **Levenshtein**: Minimum edits needed to transform one string to another
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- **SequenceMatcher**: Ratio of matching subsequences
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- **CER/WER**: Error rates at character and word levels
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### Similarity Metrics
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- **Jaccard**: Intersection over union of word sets
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- **Cosine**: Angle between text vectors
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### Semantic Similarity
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- **SentenceTransformers**: Neural models that understand meaning
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- MPNET: More accurate but slower
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- MiniLM: Faster with good accuracy
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## Use Cases
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1. **OCR Quality Assessment**: Compare OCR output with ground truth
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2. **Text Validation**: Check if generated text matches expected output
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3. **Duplicate Detection**: Find similar text passages
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4. **Content Matching**: Semantic similarity for paraphrases
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## Example Output
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```
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Actual Text: 'Hello World'
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OCR Text: 'Helo World'
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Levenshtein Distance: 1
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Levenshtein Similarity: 0.91
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```
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## Performance Notes
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- Simple algorithms (Levenshtein, Jaccard) are fast
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- SentenceTransformers require model downloading on first use
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- Models are cached after initial download
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## Troubleshooting
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- **Import errors**: Ensure all requirements are installed
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- **Model download fails**: Check internet connection
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- **Memory issues**: Use MiniLM for lower memory usage
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## Requirements
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- ComfyUI
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- Python packages: Levenshtein, scikit-learn, numpy, sentence-transformers
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## License
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MIT License
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Copyright (c) 2024
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE. |