Add EduScale models, docs, and benchmarks

Add EduScale SPAN 2x and 3x TensorFlow Lite models and supporting documentation and benchmark data. Files added: models/eduscale_span_x2.tflite, models/eduscale_span_x3.tflite, docs/MODEL_CARD.md, docs/DATASET_CARD.md, benchmarks/benchmark_x2.csv, benchmarks/benchmark_x3.csv, benchmarks/benchmark_summary.csv. README.md was expanded to describe the project, intended use, repository structure, benchmark summary, usage example, data sources, and license (Apache-2.0). Benchmarks provide per-image metrics and a short summary for mobile runtime evaluation.

README.md

@@ -1,3 +1,109 @@
 ---
 license: apache-2.0
+tags:
+  - image-super-resolution
+  - education
+  - ocr
+  - tflite
+  - mobile
+  - document-enhancement
+library_name: tensorflow
+pipeline_tag: image-to-image
 ---
+
+# EduScale AI
+
+EduScale AI is a lightweight TensorFlow Lite model release for enhancing educational images such as lecture slides, screenshots, and study material before OCR or visual inspection.
+
+This repository contains two SPAN-based super-resolution models:
+
+| Model | Scale | File |
+|---|---:|---|
+| EduScale SPAN x2 | 2x | `models/eduscale_span_x2.tflite` |
+| EduScale SPAN x3 | 3x | `models/eduscale_span_x3.tflite` |
+
+## Intended Use
+
+- Upscaling low-resolution educational slide images.
+- Improving readability before OCR workflows.
+- Mobile or edge deployment with TensorFlow Lite.
+- Research and portfolio demonstration for education-focused image restoration.
+
+These models are not a replacement for human verification of extracted text. OCR output should still be reviewed when correctness matters.
+
+## Data Sources
+
+The education slide data used around this project comes from a mix of:
+
+- Public PPT material gathered from Kaggle: [Dataset for ppt](https://www.kaggle.com/datasets/manisha717/dataset-for-ppt) by Manisha717.
+- Additional educational slide/image samples created by the project author.
+
+The Kaggle dataset page lists its license as unknown, so this repository does not redistribute the original PPT files or source images. Only model files and benchmark CSV results are included here.
+
+## Repository Structure
+
+```txt
+EduScale/
+├── README.md
+├── models/
+│   ├── eduscale_span_x2.tflite
+│   └── eduscale_span_x3.tflite
+├── benchmarks/
+│   ├── benchmark_x2.csv
+│   ├── benchmark_x3.csv
+│   └── benchmark_summary.csv
+└── docs/
+    ├── MODEL_CARD.md
+    └── DATASET_CARD.md
+```
+
+## Benchmark Summary
+
+Benchmarks were computed from the detailed per-image CSV files in `benchmarks/`.
+
+| Model | Scale | PSNR | SSIM | OCR Confidence | CER | Runtime | Device |
+|---|---:|---:|---:|---:|---:|---:|---|
+| `eduscale_span_x2` | 2x | 29.46 | 0.9786 | 91.23 | 0.0736 | 573 ms | Realme Note 50 |
+| `eduscale_span_x3` | 3x | 26.60 | 0.9642 | 89.17 | 0.1980 | 288 ms | Realme Note 50 |
+
+Metrics:
+
+- `psnr`: peak signal-to-noise ratio, higher is better.
+- `ssim`: structural similarity, higher is better.
+- `ocr_confidence`: OCR confidence after super-resolution, higher is better.
+- `cer`: character error rate after super-resolution, lower is better.
+- `runtime_ms`: average runtime in milliseconds on the benchmark device.
+
+## Benchmark Files
+
+| File | Description |
+|---|---|
+| `benchmarks/benchmark_x2.csv` | Detailed benchmark results for the 2x EduScale model |
+| `benchmarks/benchmark_x3.csv` | Detailed benchmark results for the 3x EduScale model |
+| `benchmarks/benchmark_summary.csv` | Short comparison summary of x2 and x3 model performance |
+
+## Usage
+
+Load the models with a TensorFlow Lite runtime in your target environment. The exact preprocessing and tensor shape should match the model input signature reported by your TFLite interpreter.
+
+```python
+import tensorflow as tf
+
+interpreter = tf.lite.Interpreter(model_path="models/eduscale_span_x2.tflite")
+interpreter.allocate_tensors()
+
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+print(input_details)
+print(output_details)
+```
+
+## Documentation
+
+- `docs/MODEL_CARD.md`: model details, intended use, metrics, and limitations.
+- `docs/DATASET_CARD.md`: benchmark dataset notes and CSV schema.
+
+## License
+
+This repository is released under the Apache 2.0 license.

benchmarks/benchmark_summary.csv

@@ -0,0 +1,3 @@
+model,scale,psnr,ssim,ocr_confidence,cer,runtime_ms,device
+eduscale_span_x2,2x,29.46,0.9786,91.23,0.0736,573,Realme Note 50
+eduscale_span_x3,3x,26.60,0.9642,89.17,0.1980,288,Realme Note 50

benchmarks/benchmark_x2.csv

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docs/DATASET_CARD.md

@@ -0,0 +1,67 @@
+# EduScale AI Benchmark Dataset Card
+
+## Dataset Description
+
+This repository includes benchmark result files for evaluating EduScale AI on educational slide images. The benchmark files contain per-image image quality, OCR, and runtime metrics for the 2x and 3x TensorFlow Lite models.
+
+The underlying image files are not included in this repository. The CSV files are included as benchmark evidence for model comparison.
+
+## Data Sources
+
+The project data was gathered from two source categories:
+
+| Source | Description | Redistribution Status |
+|---|---|---|
+| [Dataset for ppt](https://www.kaggle.com/datasets/manisha717/dataset-for-ppt) by Manisha717 on Kaggle | Public collection of PPT files for testing, covering varied presentation topics | Not redistributed here because the Kaggle page lists the license as unknown |
+| Creator-generated samples | Additional educational slide/image samples created by the project author | Used for project evaluation and development |
+
+The benchmark CSVs are included to document model behavior without republishing the original PPT files or source images.
+
+## Files
+
+| File | Description |
+|---|---|
+| `benchmarks/benchmark_x2.csv` | Per-image benchmark results for the 2x model |
+| `benchmarks/benchmark_x3.csv` | Per-image benchmark results for the 3x model |
+| `benchmarks/benchmark_summary.csv` | Averaged model comparison summary |
+
+## CSV Schema
+
+The detailed benchmark files use the following columns:
+
+| Column | Description |
+|---|---|
+| `file` | Evaluation image filename |
+| `psnr` | Peak signal-to-noise ratio |
+| `ssim` | Structural similarity index |
+| `runtime_ms` | Inference runtime in milliseconds |
+| `lr_conf` | OCR confidence on low-resolution input |
+| `sr_conf` | OCR confidence after super-resolution |
+| `hr_conf` | OCR confidence on high-resolution reference |
+| `baseline_cer` | Character error rate for the baseline input |
+| `sr_cer` | Character error rate after super-resolution |
+
+The summary file uses:
+
+| Column | Description |
+|---|---|
+| `model` | Model identifier |
+| `scale` | Upscaling factor |
+| `psnr` | Average PSNR |
+| `ssim` | Average SSIM |
+| `ocr_confidence` | Average `sr_conf` |
+| `cer` | Average `sr_cer` |
+| `runtime_ms` | Average runtime in milliseconds |
+| `device` | Device used for benchmark measurement |
+
+## Evaluation Device
+
+Benchmarks in `benchmark_summary.csv` are labeled as measured on:
+
+`Realme Note 50`
+
+## Known Limitations
+
+- The CSV files provide benchmark metrics, not the original image dataset.
+- Results are specific to the evaluation pipeline and device used.
+- The benchmark domain is educational slides and may not generalize to all scanned documents, handwriting, photos, or non-educational imagery.

docs/MODEL_CARD.md

@@ -0,0 +1,70 @@
+# EduScale AI Model Card
+
+## Model Details
+
+EduScale AI provides two TensorFlow Lite super-resolution models for educational image enhancement:
+
+| Model | Scale | File |
+|---|---:|---|
+| EduScale SPAN x2 | 2x | `models/eduscale_span_x2.tflite` |
+| EduScale SPAN x3 | 3x | `models/eduscale_span_x3.tflite` |
+
+The models are intended for mobile and edge use cases where low-resolution slide or study-material images need to be enhanced before reading or OCR.
+
+## Intended Use
+
+- Educational slide image enhancement.
+- OCR preprocessing for screenshots, slides, and study materials.
+- Mobile deployment using TensorFlow Lite.
+- Research, demonstration, and portfolio evaluation.
+
+## Data
+
+Project data was gathered from educational presentation material and creator-made slide/image samples. The public source identified for part of the collected data is Kaggle's [Dataset for ppt](https://www.kaggle.com/datasets/manisha717/dataset-for-ppt) by Manisha717, described as PPT files for testing across varied topics.
+
+Because the Kaggle page lists the dataset license as unknown, this model repository does not redistribute the original PPT files or source images.
+
+## Out-of-Scope Use
+
+- Medical, legal, or safety-critical document interpretation.
+- Recovering text that is not visually supported by the original image.
+- Treating OCR output as verified ground truth without review.
+
+## Benchmark Results
+
+The following values are averages from the detailed benchmark CSV files:
+
+| Model | Scale | PSNR | SSIM | OCR Confidence | CER | Runtime | Device |
+|---|---:|---:|---:|---:|---:|---:|---|
+| `eduscale_span_x2` | 2x | 29.46 | 0.9786 | 91.23 | 0.0736 | 573 ms | Realme Note 50 |
+| `eduscale_span_x3` | 3x | 26.60 | 0.9642 | 89.17 | 0.1980 | 288 ms | Realme Note 50 |
+
+Detailed results are available in:
+
+- `benchmarks/benchmark_x2.csv`
+- `benchmarks/benchmark_x3.csv`
+- `benchmarks/benchmark_summary.csv`
+
+## Metrics
+
+- `PSNR`: image reconstruction quality; higher is better.
+- `SSIM`: structural similarity; higher is better.
+- `OCR confidence`: OCR model confidence after super-resolution; higher is better.
+- `CER`: character error rate after super-resolution; lower is better.
+- `runtime_ms`: average measured inference runtime on the listed device.
+
+## Limitations
+
+- Performance can vary by camera quality, compression artifacts, text size, font, language, and lighting.
+- Super-resolution may sharpen artifacts along with useful text features.
+- OCR gains are not guaranteed for every image.
+- The benchmark set is education-focused and may not represent all document or natural-image domains.
+- Source data includes a public Kaggle PPT dataset with unknown license status, so users should review upstream terms before reusing the original PPT data directly.
+
+## Ethical Considerations
+
+Enhanced images should not be used to fabricate or overstate document accuracy. When used for OCR or study workflows, extracted text should be verified against the original source when correctness matters.
+
+## License
+
+Apache 2.0.

models/eduscale_span_x2.tflite

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+size 971384

models/eduscale_span_x3.tflite

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