# 📊 VERIFIED METRICS TABLE: Rakīm AI (رَقِيم)

Below is the verified dataset of digital metrics compiled from the project's verification sample reports, test logs, presentation JSON configurations, and service scripts.

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## 1. Character Error Rate (CER) and Model Metrics

| Metric | Value | Dataset / Sample | Source File | Confidence | Notes |
| :--- | :--- | :--- | :--- | :--- | :--- |
| **Trained Sample CER** | **3.29%** (96.71% Acc) | 3 training pages (Maghrebi) | `عينات_التحقق/النتائج.md` | **High** | Measures how well the model memorizes its training subset. |
| **Unseen Test CER** | **7.50%** (92.50% Acc) | 51 frozen test pages (Maghrebi) | `عينات_التحقق/النتائج.md` | **High** | Primary metric for general readability and model performance. |
| **Baseline exp6 (No LM) CER** | **7.48%** | RASAM Maghrebi dataset | `presentation_dashboard/data/metrics.json` | **High** | Verified in direct comparison logs. |
| **LM exp6 (With LM) CER** | **6.79%** | RASAM Maghrebi dataset | `presentation_dashboard/data/metrics.json` | **High** | Shows a **0.69%** absolute error reduction using local LM. |
| **Baseline TariMa (No LM) CER** | **10.27%** | TariMa manuscript dataset | `presentation_dashboard/data/metrics.json` | **High** | Generalization baseline for TariMa hands. |
| **LM TariMa (With LM) CER** | **8.81%** | TariMa manuscript dataset | `presentation_dashboard/data/metrics.json` | **High** | Shows a **1.46%** absolute error reduction using local LM. |
| **Legacy Model CER** | **Unusable** (Garbage Text) | Maghrebi scripts | `المقارنة_الموحدة.md` | **High** | Model `reg(96.5)` fails completely on historical Maghrebi hands. |

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## 2. Segmentation and Page Layout Metrics

| Page/Sample | Ground Truth (GT) Lines | Produced Lines (`seg_best`) | Source File | Status / Analysis |
| :--- | :--- | :--- | :--- | :--- |
| **Page 1926** (Simple Layout) | 9 lines | **11 lines** | `عينات_التحقق/النتائج.md` | Close to perfect segment alignment. |
| **Page 417** (Margins/Hawahin) | 13 lines | **43 lines** | `عينات_التحقق/النتائج.md` | Severe over-segmentation due to marginalia. |
| **Page 1982** (Dense + Margins) | 37 lines | **53 lines** | `عينات_التحقق/النتائج.md` | Over-segmentation of page borders/marginal notes. |
| **Page 1960** (Layout Split) | 26 lines (Main) | **30 lines** (26 Main / 4 Margins) | `demo_package/المقارنات_الرقمية.md` | Layout split correctly separates margins from text body. |

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## 3. Database Catalog Statistics

| Metric | Value | Source File | Status |
| :--- | :--- | :--- | :--- |
| **Total Cataloged Works** | **35,632** | `catalog_stats.json` | Compiled from OpenITI (13,364) & FIHRIST (15,543). |
| **Total Manuscript Copies** | **49,980** | `catalog_stats.json` | Total registered manuscript copies. |
| **Works with Multiple Copies** | **6,003** (16.85%) | `catalog_stats.json` | Works residing in more than one library collection. |
| **Average Copies per Work** | **1.4** | `catalog_stats.json` | Global duplication factor. |

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## 4. Execution Latency Metrics (CPU vs. GPU)

| Task | Device | Time (Seconds) | Source File / Logs | Notes |
| :--- | :--- | :--- | :--- | :--- |
| **Neural Segmentation (BLLA)** | CPU (Docker) | **~11.0s** | `demo_package/README_DEMO.md` | BLLA is structurally heavy. |
| **Text Recognition** | CPU (Docker) | **~4.0s** | `demo_package/README_DEMO.md` | Direct Kraken PyTorch execution. |
| **Total Inference Page Latency** | CPU (Docker) | **~14.0–16.0s** | `demo_package/README_DEMO.md` | Time required to process a single raw page. |
| **Fast Segmentation** | CPU (Direct) | **1.0–3.0s** | `config.py` | Settings flag `FAST_SEGMENTATION=true`. |
| **Total Inference Page Latency** | GPU (RTX 4050) | **~4.0s** | `demo_package/README_DEMO.md` | Substantial acceleration (almost 4x speedup). |
| **Inference Page Latency** | Cache Hit | **0.0s** | `ocr.py` | Immediate response if image hash matches cache database. |