| # Dyslexic Writing-Pattern Classifier (Sinhala) |
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| This module implements an **interpretable, rule-based dyslexic writing-pattern classifier** for Sinhala text. |
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| Unlike traditional machine-learning classifiers, this component focuses on **pattern inference and explainability**, rather than predictive accuracy. |
| It is designed to analyze _how_ dyslexic writing manifests, not merely _whether_ dyslexia is present. |
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| ## Purpose |
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| - Identify **dominant dyslexic writing patterns** in Sinhala text |
| - Provide **explainable, linguistically grounded analysis** |
| - Support educational and research-oriented dyslexia-aware systems |
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| This module is executed **only after** an essay has been identified as dyslexic by the Binary Dyslexia Detector. |
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| ## Core Design Principle |
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| > Dyslexia is expressed through **consistent patterns of surface-level writing errors**, not isolated mistakes. |
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| Therefore, this classifier infers patterns using **rule-based dominance of error signals**, rather than supervised learning. |
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| --- |
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| ## Writing Patterns Identified |
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| The system currently identifies the following dyslexic writing patterns: |
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| - **Orthographic Instability** |
| Frequent character omissions, additions, or diacritic loss |
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| - **Phonetic Confusion** |
| Character substitutions reflecting phonetic similarity |
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| - **Mixed Dyslexic Pattern** |
| Co-occurrence of multiple dominant error types |
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| - **No Dominant Pattern** |
| Absence of consistent dyslexic error behavior |
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| - **Word Boundary Confusion** (when applicable) |
| Spacing and word segmentation errors |
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| These patterns are derived from dyslexia-related literature and adapted for Sinhala writing. |
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| --- |
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| ## Processing Pipeline |
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| ### 1. Sentence-Level Analysis |
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| For each sentence: |
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| - Clean and dyslexic versions are compared |
| - Surface error features are extracted: |
| - Character addition |
| - Character omission |
| - Character substitution |
| - Diacritic loss |
| - Spacing issues |
| - A **rule-based inference engine** assigns a sentence-level writing pattern |
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| ### 2. Essay-Level Aggregation |
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| Because the dataset does not provide explicit essay boundaries: |
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| - Essays are approximated using **fixed-size sentence windows** (pseudo-essays) |
| - Sentence-level patterns are aggregated per essay |
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| ### 3. Dominant Pattern Classification |
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| For each essay: |
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| - The most frequent pattern is selected as the **dominant pattern** |
| - A **confidence score** is computed as: |
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| \[ |
| Confidence = \frac{\text{Number of sentences supporting dominant pattern}} |
| {\text{Total number of sentences in essay}} |
| \] |
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| - Dominance strength is categorized as: |
| - Strong Dominance |
| - Moderate Dominance |
| - Weak / Mixed |
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| --- |
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| ## Outputs |
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| For each essay, the classifier produces: |
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| - Dominant dyslexic writing pattern |
| - Pattern dominance confidence |
| - Dominance strength label |
| - Sentence-level pattern breakdown (for explainability) |
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| ### Example Output |
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| ```json |
| { |
| "dominant_pattern": "Orthographic Instability", |
| "confidence": 0.6, |
| "dominance_strength": "Strong Dominance" |
| } |
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| --- |
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| ## Evaluation Strategy |
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| This component does not use supervised evaluation metrics such as accuracy or F1-score. |
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| Reason: |
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| - Essay-level pattern labels are inferred, not manually annotated |
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| - Reporting accuracy would result in label leakage |
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| Instead, evaluation is performed using: |
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| - Pattern distribution analysis |
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| - Confidence distribution statistics |
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| - Qualitative case studies with sentence-level evidence |
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| This approach aligns with best practices in dyslexia-related linguistic analysis. |
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| ## Notebooks |
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| notebooks/ |
| ├── 01_surface_feature_extraction_and_pattern_inference_v3.ipynb |
| └── 02_essay_level_dyslexic_pattern_profiling.ipynb |
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| These notebooks document the full development and validation process. |
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| ## Limitations |
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| Essay boundaries are approximated using fixed-size sentence windows |
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| The system does not perform clinical diagnosis |
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| Pattern definitions may evolve with expert validation |
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| ## Role in the Overall System |
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| (Binary Dyslexia Detector) |
| ↓ |
| Dyslexic Essay |
| ↓ |
| Writing-Pattern Classifier |
| ↓ |
| Pattern Profile + Confidence |
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| ## Disclaimer |
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| This module is intended for research and educational purposes only and should not be used for clinical diagnosis. |
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| Generated CSV artifacts are intentionally excluded from version control and can be reproduced by executing the notebooks or pipeline. |
| ``` |
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