[C] Update READMEs

Author: zephyr-sh

README.md

@@ -68,6 +68,46 @@ For more details, please refer to the [**DocClassifier Documents**](https://docs
    pip install dist/docclassifier_docsaid-*-py3-none-any.whl
    ```
 
+## Inference
+
+> [!TIP]
+> We have designed an automatic model download feature. When the program detects that you are missing the model, it will automatically connect to our server to download it.
+
+Here is a simple example:
+
+```python
+import cv2
+from skimage import io
+from docclassifier import DocClassifier
+
+img = io.imread('https://github.com/DocsaidLab/DocClassifier/blob/main/docs/test_driver.jpg?raw=true')
+img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+model = DocClassifier()
+
+most_similar, max_score = model(img)
+print(f'most_similar: {most_similar}, max_score: {max_score:.4f}')
+# >>> most_similar: None, max_score: 0.0000
+```
+
+By default, this example returns `None` and `0.0000` because the difference between our default registration data and the input image is significant. Therefore, the model finds the similarity between the image and the registration data to be very low.
+
+In this case, you may consider lowering the `threshold` parameter:
+
+```python
+model = DocClassifier(
+    threshold=0.6
+)
+
+# Re-run the inference
+most_similar, max_score = model(img)
+print(f'most_similar: {most_similar}, max_score: {max_score:.4f}')
+# >>> most_similar: Taiwan driver's license front, max_score: 0.6116
+```
+
+> [!TIP]
+> MRZScanner has been encapsulated with `__call__`, so you can directly call the instance for inference.
+
 ## Model Design
 
 Creating a comprehensive model involves multiple adjustments and design iterations.

README_tw.md

@@ -68,6 +68,50 @@ DocClassifier 是一個基於 Metric Learning 技術的文件圖像分類系統
    pip install dist/docclassifier_docsaid-*-py3-none-any.whl
    ```
 
+## 模型推論
+
+> [!TIP]
+> 我們有設計了自動下載模型的功能，當程式檢查你缺少模型時，會自動連接到我們的伺服器進行下載。
+
+確保註冊資料已經準備好之後，我們就可以開始進行模型推論。
+
+以下是一個簡單的範例：
+
+```python
+import cv2
+from skimage import io
+from docclassifier import DocClassifier
+
+img = io.imread('https://github.com/DocsaidLab/DocClassifier/blob/main/docs/test_driver.jpg?raw=true')
+img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+model = DocClassifier()
+
+most_similar, max_score = model(img)
+print(f'most_similar: {most_similar}, max_score: {max_score:.4f}')
+# >>> most_similar: None, max_score: 0.0000
+```
+
+在預設的情況下，這個範例會返回 `None` 和 `0.0000`，這是因為我們預設的註冊資料和輸入圖片差異非常大。因此模型判斷這張圖片和註冊資料之間的相似性非常低。
+
+這種時候你可以考慮降低 `threshold` 參數：
+
+```python
+model = DocClassifier(
+    threshold=0.6
+)
+
+# 重新進行推論
+most_similar, max_score = model(img)
+print(f'most_similar: {most_similar}, max_score: {max_score:.4f}')
+# >>> most_similar: 台灣駕照正面, max_score: 0.6116
+```
+
+這次你就會得到一個標籤名稱和一個分數：`台灣駕照正面` 和 `0.6116`。這個分數代表了輸入圖片和註冊資料之間的相似性。
+
+> [!TIP]
+> DocClassifier 已經用 `__call__` 進行了封裝，因此你可以直接呼叫實例進行推論。
+
 ## 模型設計
 
 一個較為完整的模型功能，都不是一蹴可幾的，中間必須經過多次的調整和設計。