Implement OCR functionality with Gradio interface and model downloads

app.py

@@ -32,14 +32,26 @@ def preprocess_image(image):
     # Resize or pad image to desired size if necessary
     return gray
 
+import cv2
+from rapidocr_onnxruntime import RapidOCR
+
+# Initialize the RapidOCR engine
+ocr_engine = RapidOCR()
+
 def ocr_predict(image):
-    # Preprocess the image
-    preprocessed_image = preprocess_image(image)
-    # Perform detection, classification, and recognition using the ONNX models
-    # This is a placeholder for the actual OCR pipeline
-    # Replace with your model's inference code
-    text = "Detected text goes here"
-    return text
+    """
+    Perform OCR on the input image and return the extracted text.
+    """
+    # Convert the image from RGB to BGR format as OpenCV uses BGR
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    # Perform OCR
+    result, _ = ocr_engine(image_bgr)
+
+    # Extract text from the result
+    extracted_text = "\n".join([item[1] for item in result])
+
+    return extracted_text
 
 # Define Gradio interface
 iface = gr.Interface(

requirements.txt

@@ -4,4 +4,5 @@ onnxruntime
 numpy
 opencv-python
 torch
-huggingface-hub
+huggingface-hub
+rapidocr_onnxruntime