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README.md

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+---
+language: zh
+tags:
+- image-classification
+- chinese
+- handwriting
+- hsk1
+- pytorch
+license: mit
+---
+
+# Chinese Handwriting Recognition – HSK 1
+
+A CNN image classifier trained on the CASIA-HWDB dataset
+to recognise **178 HSK-1 level Chinese characters** from handwritten images.
+
+## Model Architecture
+- Backbone: **ResNet-18** (pretrained on ImageNet, fine-tuned)
+- Final layer: Dropout(0.4) → Linear(178 classes)
+- Input: RGB image resized to 64 × 64
+
+## Performance
+- Best Validation Accuracy: **98.13%**
+
+## Training Setup
+| Hyperparameter | Value |
+|---|---|
+| Optimizer | AdamW |
+| Learning rate | 1e-3 |
+| Weight decay | 1e-4 |
+| Scheduler | OneCycleLR |
+| Epochs | 15 |
+| Batch size | 128 |
+| GPUs | T4 x2 |
+| Augmentation | RandomRotation(10°), RandomAffine, ColorJitter |
+
+## Usage
+
+```python
+import torch
+from PIL import Image
+from torchvision import transforms
+import torch.nn as nn
+import torchvision.models as models
+from huggingface_hub import hf_hub_download
+
+# ── Download checkpoint ───────────────────────────────
+ckpt_path  = hf_hub_download('ChrisMoe/Chinese_handwriting_model', 'chinese_hsk1_model.pth')
+checkpoint = torch.load(ckpt_path, map_location='cpu')
+
+# ── Rebuild model ─────────────────────────────────────
+class ChineseCharCNN(nn.Module):
+    def __init__(self, num_classes, dropout=0.4):
+        super().__init__()
+        backbone = models.resnet18(weights=None)
+        in_features = backbone.fc.in_features
+        backbone.fc = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(in_features, num_classes)
+        )
+        self.model = backbone
+    def forward(self, x):
+        return self.model(x)
+
+model = ChineseCharCNN(num_classes=checkpoint['num_classes'])
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+idx2char = checkpoint['idx2char']
+
+# ── Inference ─────────────────────────────────────────
+transform = transforms.Compose([
+    transforms.Resize((64, 64)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
+])
+img    = Image.open('your_character.png').convert('RGB')
+tensor = transform(img).unsqueeze(0)
+with torch.no_grad():
+    logits = model(tensor)
+pred_idx = logits.argmax(1).item()
+print('Predicted character:', idx2char[pred_idx])
+```