---
license: creativeml-openrail-m
---

# Model Card for Kanji_ETL8G
### Summary:
```
ETL8B
 - 153916 samples
 - 956 classes (hiragana and kanji)
 - 161 samples each class
 - image_width: 64px
 - image_height: 63px
```

## Model Details
- **Model Name:** Kanji_ETL8G
- **Version:** 1.0.0
- **Model Type:** Neural Network 
- **Framework:** PyTorch
  
## Model Description
This model is trained on a dataset derived from the ETL8G dataset to recognize Kanji characters from 64x64 grayscale images. The primary use-case is for optical character recognition (OCR) for handwritten Kanji characters.

## Intended Use
The primary application of this model is for OCR tasks to recognize handwritten Kanji characters in images, with potential extensions for applications like smart dictionary lookup, handwriting-based user authentication, and so on.

## Limitations
This model might have limitations regarding:
- Variability in handwriting styles not present in the training set. (161 samples per character/class were used)
- Noises and artifacts in input images.
- Characters written in unconventional ways.
## Data Details
### Training Data:
- **Dataset:** Derived from the ETL8G dataset (hhttp://etlcdb.db.aist.go.jp/specification-of-etl-8)
- **Size:** 153916 samples
- **Data Type:** 64x64 grayscale images of handwritten Kanji characters 
- **Labels:** 956 unique characters (classes)
## Model Files
- **PyTorch Model:** Kanji_ETL8G.pth
- **ONNX Model:** Kanji_ETL8G.onnx
- **CoreML Model:** next effort....
## Usage
```python
import torch
model = torch.load('Kanji_ETL8G.pth')
model.eval()
# Assuming input image tensor is `input_tensor`
output = model(input_tensor)
predicted_label = torch.argmax(output).item()
```