onnx license:creativeml-openrail-m region:us
---
license: creativeml-openrail-m
---
# Model Card for Kanji_ETL9G

### Summary:
```
ETL9G
 : 607200 samples
 : 3036 classes (hiragana and kanji)
 : 200 samples each class
 : record_length: 8199 bytes
 : image_width: 64px
 : image_height: 64px
```



## Model Details
- **Model Name:** Kanji_ETL9G
- **Version:** 1.0.0
- **Model Type:** Neural Network 
- **Framework:** PyTorch

## Model Description
This model is trained on a dataset derived from the ETL9G 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. (200 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 ETL9G dataset (http://etlcdb.db.aist.go.jp/specification-of-etl-9)
- **Size:** 607200 samples
- **Data Type:** 64x64 grayscale images of handwritten Kanji characters (images were resized from 128x127 due to technical limitations)
- **Labels:** 3036 unique characters (classes)

## Model Files
- **PyTorch Model:** Kanji_ETL9G.pth
- **ONNX Model:** Kanji_ETL9G.onnx
- **CoreML Model:** next effort....

## Usage
```python
import torch
model = torch.load('Kanji_ETL9G.pth')
model.eval()

# Assuming input image tensor is `input_tensor`
output = model(input_tensor)
predicted_label = torch.argmax(output).item()
