# Routing Strategies & Feature Modeling
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# Cost-Aware Routing Strategies & Models
## 1. Feature Engineering (54 Features)
The routing engine extracts 54 computationally inexpensive features prior to invoking any LLM:
1. **OCR Confidence Metrics:** Mean confidence, minimum token confidence, standard deviation, count of low-confidence tokens (<80%, <50%).
2. **Lexical & Linguistic Features:** Out-of-vocabulary (OOV) ratio against historical lexicon, archaic character frequency (e.g. `ſ`, `œ`, ligature anomalies).
3. **Statistical Text Metrics:** Punctuation density, digit-to-letter ratios, average word length, uppercase token anomalies.
4. **Layout Context:** Bounding box coordinates, line height variance, bounding box density.
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## 2. Evaluated Router Models
The router was benchmarked across multiple learning paradigms:
- **Lasso Regression (L1):** Sparse feature selection, highly interpretable, *<0.1*s inference time on single CPU core.
- **Ridge Regression (L2):** Smooth shrinkage across correlated confidence features.
- **Support Vector Machines (SVM):** Non-linear RBF kernel separating degradation clusters.
- **Multi-Layer Perceptron (NN):** 2-layer feedforward network predicting expected CER reduction.
- **ConfBERT:** Fine-tuned lightweight language model scoring token perplexity and error likelihood.
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## 3. Results Summary
- **Selective Efficiency:** At *\\tau = 0*, the Lasso router skips **40.2% of segments**, reducing API costs by 38.5% while degrading final CER by only **0.20 percentage points** (3.12% vs 2.92%).
- **Oracle Tracking:** The regression router achieves *91.4\\%* of the theoretical maximum gain achievable by an omniscient oracle.
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## Empirical Routing Curves & Threshold Analysis
### Regression-Based Routing Performance
### Threshold Tuning & Boundary Optimization