Create README.md

README.md

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+---
+language:
+- tr
+- otk
+tags:
+- ocr
+- old-turkic
+- gokturk
+- resnet
+- onnx
+- computer-vision
+- image-classification
+metrics:
+- accuracy
+---
+
+*Work in progress.*
+
+# Gokturk ResNet OCR Model
+
+This is a **ResNet-based** OCR model specifically trained to recognize **Old Turkic (Gokturk)** script characters. It is optimized for inference using **ONNX Runtime**, making it highly portable and efficient.
+
+## Model Description
+
+- **Repository:** [ocr-gokturk](https://github.com/EdgeTypE/ocr-gokturk)
+- **Task:** Optical Character Recognition (OCR) / Image Classification
+- **Classes:** 75 characters (Unicode range `U+10C00` – `U+10C4A`)
+- **Input Shape:** [(Batch, 64, 64, 1)](file:///a:/Users/Edige/GitHub/ocr-gokturk/src/ocr.rs#36-60) (Grayscale)
+- **Format:** ONNX
+
+## How to use
+
+This model is primarily used within the [ocr-gokturk](https://github.com/EdgeTypE/ocr-gokturk) Rust project.
+
+### In Rust (with `ort` crate)
+
+```rust
+use ort::session::Session;
+
+let session = Session::builder()?
+    .commit_from_file("gokturk_resnet_v1.onnx")?;
+```
+
+### In Python (with `onnxruntime`)
+
+```python
+import onnxruntime as ort
+import numpy as np
+
+session = ort.InferenceSession("gokturk_resnet_v1.onnx")
+# Expects a 64x64 grayscale image normalized to [0, 1]
+# input_data shape: (1, 64, 64, 1)
+result = session.run(None, {"input_1": input_data})
+```
+
+## Dataset and Training
+
+The model was trained on a curated dataset of Gokturk script characters, covering various styles and weights of the Orkhon and Yenisei variants.
+
+## Files
+- [gokturk_resnet_v1.onnx](file:///a:/Users/Edige/GitHub/ocr-gokturk/gokturk_resnet_v1.onnx): The main inference model.
+- [model_labels.json](file:///a:/Users/Edige/GitHub/ocr-gokturk/model_labels.json): Mapping of model output indices to character labels.