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README.md

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+---
+language: zh
+tags:
+  - image-classification
+  - handwriting
+  - chinese
+  - keras
+  - tensorflow
+  - open-set-recognition
+  - resnet
+license: mit
+---
+
+# Chinese Handwriting Recognition — HSK1 v3 (ResNet CNN)
+
+A ResNet-style CNN trained on HWDB1.0 to recognise **178 Chinese characters** + **Unknown**.
+
+## What's new in v3
+| Feature | v2 | v3 |
+|---|---|---|
+| Architecture | Plain CNN | ResNet-style residual blocks |
+| Loss | Cross-entropy | Cross-entropy + label smoothing (0.1) |
+| Optimizer | Adam | AdamW (weight decay 1e-4) |
+| Augmentation | Rotation, shift, zoom | + Shear, stronger zoom |
+| Temperature scaling | Yes (buggy) | Removed (not needed) |
+| Config | Scattered | Central CFG dict |
+
+## Model details
+| Item | Value |
+|---|---|
+| Input | 40×40 grayscale image |
+| Classes | 179 (178 Chinese characters + Unknown) |
+| Framework | Keras / TensorFlow |
+| Confidence threshold | 0.3 |
+| OOD training data | EMNIST Balanced (8% of training set) |
+
+## Quick start
+```python
+import numpy as np, json
+import tensorflow as tf
+from tensorflow import keras
+
+model     = keras.models.load_model('chinese_hsk1_model_v3.keras')
+label_map = json.load(open('label_map_v3.json', encoding='utf-8'))
+cfg       = json.load(open('config_v3.json'))
+THRESHOLD = cfg['threshold']
+
+def predict(img_gray):
+    x     = img_gray.astype('float32') / 255.0
+    x     = x.reshape(1, cfg['img_size'], cfg['img_size'], 1)
+    probs = model.predict(x)[0]
+    conf  = probs.max()
+    idx   = probs.argmax()
+    char  = label_map[str(idx)]
+    if char == 'Unknown' or conf < THRESHOLD:
+        return 'Unknown', conf
+    return char, conf
+```