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license: creativeml-openrail-m |
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# Model Card for Kanji_ETL9G |
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### Summary: |
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``` |
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ETL9G |
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: 607200 samples |
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: 3036 classes (hiragana and kanji) |
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: 200 samples each class |
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: record_length: 8199 bytes |
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: image_width: 64px |
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: image_height: 64px |
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``` |
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## Model Details |
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- **Model Name:** Kanji_ETL9G |
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- **Version:** 1.0.0 |
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- **Model Type:** Neural Network |
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- **Framework:** PyTorch |
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## Model Description |
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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. |
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## Intended Use |
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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. |
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## Limitations |
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This model might have limitations regarding: |
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- Variability in handwriting styles not present in the training set. (200 samples per character/class were used) |
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- Noises and artifacts in input images. |
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- Characters written in unconventional ways. |
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## Data Details |
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### Training Data: |
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- **Dataset:** Derived from the ETL9G dataset (http://etlcdb.db.aist.go.jp/specification-of-etl-9) |
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- **Size:** 607200 samples |
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- **Data Type:** 64x64 grayscale images of handwritten Kanji characters (images were resized from 128x127 due to technical limitations) |
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- **Labels:** 3036 unique characters (classes) |
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## Model Files |
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- **PyTorch Model:** Kanji_ETL9G.pth |
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- **ONNX Model:** Kanji_ETL9G.onnx |
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- **CoreML Model:** next effort.... |
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## Usage |
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```python |
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import torch |
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model = torch.load('Kanji_ETL9G.pth') |
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model.eval() |
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# Assuming input image tensor is `input_tensor` |
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output = model(input_tensor) |
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predicted_label = torch.argmax(output).item() |
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