Delete EasyOpticalCharacterRecognition.py

EasyOpticalCharacterRecognition.py

@@ -1,66 +0,0 @@
-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
-
-# === Load model and label encoder ===
-model_path = 'MobileNetBest_Model.h5'
-pkl_path = 'MobileNet_Label_Encoder.pkl'
-
-model = load_model(model_path)
-print("✅ Model loaded.")
-
-# === 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