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

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()