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EasyOpticalCharacterRecognition.py

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+import cv2
+import numpy as np
+import easyocr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+import pickle
+import os
+from google.colab import drive
+
+# === Mount Google Drive ===
+drive.mount('/content/drive')
+
+# === Load model and label encoder ===
+model_path = '/content/drive/My Drive/ML1_Project/MobileNet/Model6/MobileNetBest_Model.h5'
+pkl_path = '/content/drive/My Drive/ML1_Project/MobileNet/Model6/MobileNet_Label_Encoder.pkl'
+
+model = load_model(model_path)
+print("✅ Model loaded.")
+
+if os.path.exists(pkl_path):
+    with open(pkl_path, 'rb') as f:
+        label_map = pickle.load(f)
+        index_to_label = {v: k for k, v in label_map.items()}
+    print("✅ Label encoder loaded.")
+else:
+    index_to_label = {0: "Handwritten", 1: "Computerized"}
+    print("⚠️ Label encoder not found, using default mapping.")
+
+# === Classification function ===
+def classify_text_region(region_img):
+    try:
+        region_img = cv2.resize(region_img, (224, 224))
+        region_img = region_img.astype("float32") / 255.0
+        region_img = img_to_array(region_img)
+        region_img = np.expand_dims(region_img, axis=0)
+
+        preds = model.predict(region_img)
+
+        if preds.shape[-1] == 1:
+            return "Computerized" if preds[0][0] > 0.5 else "Handwritten"
+        else:
+            class_idx = np.argmax(preds[0])
+            return index_to_label.get(class_idx, "Unknown")
+    except Exception as e:
+        print("❌ Classification error:", e)
+        return "Unknown"
+
+# === OCR + Annotation ===
+def AnnotatedTextDetection_EasyOCR_from_array(img):
+    reader = easyocr.Reader(['en'], gpu=False)
+    results = reader.readtext(img)
+    annotated_results = []
+
+    for (bbox, text, conf) in results:
+        if conf < 0.3 or text.strip() == "":
+            continue
+
+        x1, y1 = map(int, bbox[0])
+        x2, y2 = map(int, bbox[2])
+        w, h = x2 - x1, y2 - y1
+
+        crop = img[y1:y2, x1:x2]
+        if crop.size == 0:
+            continue
+
+        label = classify_text_region(crop)
+        annotated_results.append(f"{text.strip()} → {label}")
+
+        color = (0, 255, 0) if label == "Computerized" else (255, 0, 0)
+        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+        cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1)
+
+    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB), "\n".join(annotated_results)
+
+# === Main image processing function ===
+def process_image(input_image):
+    img = cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR)
+    result_img, text_result = AnnotatedTextDetection_EasyOCR_from_array(img)
+    return result_img, text_result
+
+

MobileNetBest_Model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11fbc2c939321af199451bacab05f6533e6562310e41a498baf1e3cb80cf8b59
+size 28405792