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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
# === Load model and label encoder ===
model_path = 'MobileNetBest_Model.h5'
pkl_path = '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
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