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: OldTurkicOCR
Task: Optical Character Recognition (OCR) / Image Classification
Classes: 75 characters (Unicode range U+10C00 – U+10C4A)
Input Shape: (Batch, 64, 64, 1) (Grayscale)
Format: ONNX

How to use

This model is primarily used within the OldTurkicOCR Rust project.

In Rust (with `ort` crate)

use ort::session::Session;

let session = Session::builder()?
    .commit_from_file("gokturk_resnet_v1.onnx")?;

In Python (with `onnxruntime`)

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: The main inference model.
model_labels.json: Mapping of model output indices to character labels.

Downloads last month: -; Downloads are not tracked for this model. How to track