Sanjayraju30 commited on
Commit
dc1f7da
·
verified ·
1 Parent(s): 4b8d12c

Update ocr_engine.py

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Files changed (1) hide show
  1. ocr_engine.py +48 -170
ocr_engine.py CHANGED
@@ -32,30 +32,30 @@ def estimate_brightness(img):
32
  return np.mean(gray)
33
 
34
  def preprocess_image(img):
35
- """Preprocess image with simplified, robust contrast enhancement."""
36
  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
37
  brightness = estimate_brightness(img)
38
 
39
- # Apply mild CLAHE for contrast
40
- clahe_clip = 8.0 if brightness < 90 else 4.0
41
  clahe = cv2.createCLAHE(clipLimit=clahe_clip, tileGridSize=(8, 8))
42
  enhanced = clahe.apply(gray)
43
  save_debug_image(enhanced, "01_preprocess_clahe")
44
 
45
- # Light blur to reduce noise
46
- blurred = cv2.GaussianBlur(enhanced, (5, 5), 0)
47
  save_debug_image(blurred, "02_preprocess_blur")
48
 
49
- # Dynamic thresholding with larger block size for small displays
50
- block_size = max(7, min(31, int(img.shape[0] / 20) * 2 + 1))
51
  thresh = cv2.adaptiveThreshold(
52
  blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
53
- cv2.THRESH_BINARY_INV, block_size, 3
54
  )
55
 
56
- # Minimal morphological operations
57
- kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
58
- thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
59
  save_debug_image(thresh, "03_preprocess_morph")
60
  return thresh, enhanced
61
 
@@ -63,12 +63,12 @@ def correct_rotation(img):
63
  """Correct image rotation using edge detection."""
64
  try:
65
  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
66
- edges = cv2.Canny(gray, 30, 100, apertureSize=3)
67
- lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=25, minLineLength=15, maxLineGap=10)
68
  if lines is not None:
69
  angles = [np.arctan2(line[0][3] - line[0][1], line[0][2] - line[0][0]) * 180 / np.pi for line in lines]
70
  angle = np.median(angles)
71
- if abs(angle) > 0.3:
72
  h, w = img.shape[:2]
73
  center = (w // 2, h // 2)
74
  M = cv2.getRotationMatrix2D(center, angle, 1.0)
@@ -81,21 +81,21 @@ def correct_rotation(img):
81
  return img
82
 
83
  def detect_roi(img):
84
- """Detect region of interest with broader contour analysis."""
85
  try:
86
  save_debug_image(img, "04_original")
87
  thresh, enhanced = preprocess_image(img)
88
  brightness_map = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
89
- block_sizes = [max(7, min(31, int(img.shape[0] / s) * 2 + 1)) for s in [5, 10, 20]]
90
  valid_contours = []
91
  img_area = img.shape[0] * img.shape[1]
92
 
93
  for block_size in block_sizes:
94
  temp_thresh = cv2.adaptiveThreshold(
95
  enhanced, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
96
- cv2.THRESH_BINARY_INV, block_size, 3
97
  )
98
- kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
99
  temp_thresh = cv2.morphologyEx(temp_thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
100
  save_debug_image(temp_thresh, f"05_roi_threshold_block{block_size}")
101
  contours, _ = cv2.findContours(temp_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
@@ -105,15 +105,15 @@ def detect_roi(img):
105
  x, y, w, h = cv2.boundingRect(c)
106
  roi_brightness = np.mean(brightness_map[y:y+h, x:x+w])
107
  aspect_ratio = w / h
108
- if (50 < area < (img_area * 0.95) and
109
- 0.05 <= aspect_ratio <= 20.0 and w > 20 and h > 8 and roi_brightness > 15):
110
  valid_contours.append((c, area * roi_brightness))
111
  logging.debug(f"Contour (block {block_size}): Area={area}, Aspect={aspect_ratio:.2f}, Brightness={roi_brightness:.2f}")
112
 
113
  if valid_contours:
114
  contour, _ = max(valid_contours, key=lambda x: x[1])
115
  x, y, w, h = cv2.boundingRect(contour)
116
- padding = max(5, min(20, int(min(w, h) * 0.4)))
117
  x, y = max(0, x - padding), max(0, y - padding)
118
  w, h = min(w + 2 * padding, img.shape[1] - x), min(h + 2 * padding, img.shape[0] - y)
119
  roi_img = img[y:y+h, x:x+w]
@@ -130,183 +130,61 @@ def detect_roi(img):
130
  return img, None
131
 
132
  def detect_digit_template(digit_img, brightness):
133
- """Digit recognition with expanded template matching."""
134
  try:
135
  h, w = digit_img.shape
136
  if h < 5 or w < 2:
137
  logging.debug("Digit image too small for template matching.")
138
  return None
139
 
140
- # Expanded digit templates for seven-segment display variations
141
  digit_templates = {
142
- '0': [
143
- np.array([[1, 1, 1, 1, 1],
144
- [1, 0, 0, 0, 1],
145
- [1, 0, 0, 0, 1],
146
- [1, 0, 0, 0, 1],
147
- [1, 1, 1, 1, 1]], dtype=np.float32),
148
- np.array([[1, 1, 1, 1],
149
- [1, 0, 0, 1],
150
- [1, 0, 0, 1],
151
- [1, 0, 0, 1],
152
- [1, 1, 1, 1]], dtype=np.float32)
153
- ],
154
- '1': [
155
- np.array([[0, 0, 1, 0, 0],
156
- [0, 0, 1, 0, 0],
157
- [0, 0, 1, 0, 0],
158
- [0, 0, 1, 0, 0],
159
- [0, 0, 1, 0, 0]], dtype=np.float32),
160
- np.array([[0, 1, 0],
161
- [0, 1, 0],
162
- [0, 1, 0],
163
- [0, 1, 0],
164
- [0, 1, 0]], dtype=np.float32)
165
- ],
166
- '2': [
167
- np.array([[1, 1, 1, 1, 1],
168
- [0, 0, 0, 1, 1],
169
- [1, 1, 1, 1, 1],
170
- [1, 1, 0, 0, 0],
171
- [1, 1, 1, 1, 1]], dtype=np.float32),
172
- np.array([[1, 1, 1, 1],
173
- [0, 0, 1, 1],
174
- [1, 1, 1, 1],
175
- [1, 1, 0, 0],
176
- [1, 1, 1, 1]], dtype=np.float32)
177
- ],
178
- '3': [
179
- np.array([[1, 1, 1, 1, 1],
180
- [0, 0, 0, 1, 1],
181
- [1, 1, 1, 1, 1],
182
- [0, 0, 0, 1, 1],
183
- [1, 1, 1, 1, 1]], dtype=np.float32),
184
- np.array([[1, 1, 1, 1],
185
- [0, 0, 1, 1],
186
- [1, 1, 1, 1],
187
- [0, 0, 1, 1],
188
- [1, 1, 1, 1]], dtype=np.float32)
189
- ],
190
- '4': [
191
- np.array([[1, 1, 0, 0, 1],
192
- [1, 1, 0, 0, 1],
193
- [1, 1, 1, 1, 1],
194
- [0, 0, 0, 0, 1],
195
- [0, 0, 0, 0, 1]], dtype=np.float32),
196
- np.array([[1, 0, 0, 1],
197
- [1, 0, 0, 1],
198
- [1, 1, 1, 1],
199
- [0, 0, 0, 1],
200
- [0, 0, 0, 1]], dtype=np.float32)
201
- ],
202
- '5': [
203
- np.array([[1, 1, 1, 1, 1],
204
- [1, 1, 0, 0, 0],
205
- [1, 1, 1, 1, 1],
206
- [0, 0, 0, 1, 1],
207
- [1, 1, 1, 1, 1]], dtype=np.float32),
208
- np.array([[1, 1, 1, 1],
209
- [1, 1, 0, 0],
210
- [1, 1, 1, 1],
211
- [0, 0, 1, 1],
212
- [1, 1, 1, 1]], dtype=np.float32)
213
- ],
214
- '6': [
215
- np.array([[1, 1, 1, 1, 1],
216
- [1, 1, 0, 0, 0],
217
- [1, 1, 1, 1, 1],
218
- [1, 0, 0, 1, 1],
219
- [1, 1, 1, 1, 1]], dtype=np.float32),
220
- np.array([[1, 1, 1, 1],
221
- [1, 1, 0, 0],
222
- [1, 1, 1, 1],
223
- [1, 0, 1, 1],
224
- [1, 1, 1, 1]], dtype=np.float32)
225
- ],
226
- '7': [
227
- np.array([[1, 1, 1, 1, 1],
228
- [0, 0, 0, 0, 1],
229
- [0, 0, 0, 0, 1],
230
- [0, 0, 0, 0, 1],
231
- [0, 0, 0, 0, 1]], dtype=np.float32),
232
- np.array([[1, 1, 1, 1],
233
- [0, 0, 0, 1],
234
- [0, 0, 0, 1],
235
- [0, 0, 0, 1],
236
- [0, 0, 0, 1]], dtype=np.float32)
237
- ],
238
- '8': [
239
- np.array([[1, 1, 1, 1, 1],
240
- [1, 0, 0, 0, 1],
241
- [1, 1, 1, 1, 1],
242
- [1, 0, 0, 0, 1],
243
- [1, 1, 1, 1, 1]], dtype=np.float32),
244
- np.array([[1, 1, 1, 1],
245
- [1, 0, 0, 1],
246
- [1, 1, 1, 1],
247
- [1, 0, 0, 1],
248
- [1, 1, 1, 1]], dtype=np.float32)
249
- ],
250
- '9': [
251
- np.array([[1, 1, 1, 1, 1],
252
- [1, 0, 0, 0, 1],
253
- [1, 1, 1, 1, 1],
254
- [0, 0, 0, 1, 1],
255
- [1, 1, 1, 1, 1]], dtype=np.float32),
256
- np.array([[1, 1, 1, 1],
257
- [1, 0, 0, 1],
258
- [1, 1, 1, 1],
259
- [0, 0, 1, 1],
260
- [1, 1, 1, 1]], dtype=np.float32)
261
- ],
262
- '.': [
263
- np.array([[0, 0, 0],
264
- [0, 1, 0],
265
- [0, 0, 0]], dtype=np.float32),
266
- np.array([[0, 0],
267
- [1, 0],
268
- [0, 0]], dtype=np.float32)
269
- ]
270
  }
271
 
272
- # Try multiple sizes for digit image
273
  sizes = [(5, 5), (4, 4), (3, 3)] if h > w else [(3, 3), (2, 2)]
274
  best_match, best_score = None, -1
275
  for size in sizes:
276
  digit_img_resized = cv2.resize(digit_img, size, interpolation=cv2.INTER_AREA)
277
- digit_img_resized = (digit_img_resized > 100).astype(np.float32) # Binarize
278
 
279
  for digit, templates in digit_templates.items():
280
  for template in templates:
281
- if digit == '.' and size[0] > 3:
282
- continue
283
- if digit != '.' and size[0] <= 3:
284
- continue
285
  if template.shape[0] != size[0] or template.shape[1] != size[1]:
286
  continue
287
  result = cv2.matchTemplate(digit_img_resized, template, cv2.TM_CCOEFF_NORMED)
288
  _, max_val, _, _ = cv2.minMaxLoc(result)
289
- if max_val > 0.55 and max_val > best_score: # Further lowered threshold
290
  best_score = max_val
291
  best_match = digit
292
  logging.debug(f"Template match: {best_match}, Score: {best_score:.2f}")
293
- return best_match if best_score > 0.55 else None
294
  except Exception as e:
295
  logging.error(f"Template digit detection failed: {str(e)}")
296
  return None
297
 
298
  def perform_ocr(img, roi_bbox):
299
- """Perform OCR with Tesseract and robust template fallback."""
300
  try:
301
  thresh, enhanced = preprocess_image(img)
302
  brightness = estimate_brightness(img)
303
  pil_img = Image.fromarray(enhanced)
304
  save_debug_image(pil_img, "07_ocr_input")
305
 
306
- # Try multiple Tesseract configurations
307
  configs = [
308
  r'--oem 3 --psm 7 -c tessedit_char_whitelist=0123456789.', # Single line
309
- r'--oem 3 --psm 6 -c tessedit_char_whitelist=0123456789.' # Block of text
 
310
  ]
311
  for config in configs:
312
  text = pytesseract.image_to_string(pil_img, config=config)
@@ -321,13 +199,13 @@ def perform_ocr(img, roi_bbox):
321
  logging.info(f"Validated Tesseract text: {text}, Confidence: {confidence:.2f}%")
322
  return text, confidence
323
 
324
- # Fallback to template-based detection
325
  logging.info("Tesseract failed, using template-based detection.")
326
  contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
327
  digits_info = []
328
  for c in contours:
329
  x, y, w, h = cv2.boundingRect(c)
330
- if w > 4 and h > 5 and 0.03 <= w/h <= 4.0:
331
  digits_info.append((x, x+w, y, y+h))
332
 
333
  if digits_info:
@@ -344,7 +222,7 @@ def perform_ocr(img, roi_bbox):
344
  digit = detect_digit_template(digit_crop, brightness)
345
  if digit:
346
  recognized_text += digit
347
- elif x_min - prev_x_max < 10 and prev_x_max != -float('inf'):
348
  recognized_text += '.'
349
  prev_x_max = x_max
350
 
@@ -365,19 +243,19 @@ def perform_ocr(img, roi_bbox):
365
  return None, 0.0
366
 
367
  def extract_weight_from_image(pil_img):
368
- """Extract weight from any digital scale image."""
369
  try:
370
  img = np.array(pil_img)
371
  img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
372
  save_debug_image(img, "00_input_image")
373
  img = correct_rotation(img)
374
  brightness = estimate_brightness(img)
375
- conf_threshold = 0.65 if brightness > 70 else 0.45
376
 
377
  # Try ROI-based detection
378
  roi_img, roi_bbox = detect_roi(img)
379
  if roi_bbox:
380
- conf_threshold *= 1.15 if (roi_bbox[2] * roi_bbox[3]) > (img.shape[0] * img.shape[1] * 0.05) else 1.0
381
 
382
  result, confidence = perform_ocr(roi_img, roi_bbox)
383
  if result and confidence >= conf_threshold * 100:
@@ -390,10 +268,10 @@ def extract_weight_from_image(pil_img):
390
  except ValueError:
391
  logging.warning(f"Invalid weight format: {result}")
392
 
393
- # Full image fallback
394
  logging.info("Primary OCR failed, using full image fallback.")
395
  result, confidence = perform_ocr(img, None)
396
- if result and confidence >= conf_threshold * 0.85 * 100:
397
  try:
398
  weight = float(result)
399
  if 0.001 <= weight <= 5000:
 
32
  return np.mean(gray)
33
 
34
  def preprocess_image(img):
35
+ """Preprocess image with enhanced contrast and adaptive thresholding."""
36
  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
37
  brightness = estimate_brightness(img)
38
 
39
+ # Apply CLAHE with dynamic clip limit
40
+ clahe_clip = 10.0 if brightness < 80 else 5.0
41
  clahe = cv2.createCLAHE(clipLimit=clahe_clip, tileGridSize=(8, 8))
42
  enhanced = clahe.apply(gray)
43
  save_debug_image(enhanced, "01_preprocess_clahe")
44
 
45
+ # Stronger blur to reduce noise
46
+ blurred = cv2.GaussianBlur(enhanced, (7, 7), 1.0)
47
  save_debug_image(blurred, "02_preprocess_blur")
48
 
49
+ # Adaptive thresholding with larger block size
50
+ block_size = max(11, min(41, int(img.shape[0] / 15) * 2 + 1))
51
  thresh = cv2.adaptiveThreshold(
52
  blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
53
+ cv2.THRESH_BINARY_INV, block_size, 5
54
  )
55
 
56
+ # Morphological operations for better digit separation
57
+ kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
58
+ thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
59
  save_debug_image(thresh, "03_preprocess_morph")
60
  return thresh, enhanced
61
 
 
63
  """Correct image rotation using edge detection."""
64
  try:
65
  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
66
+ edges = cv2.Canny(gray, 50, 150, apertureSize=3)
67
+ lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=20, minLineLength=10, maxLineGap=5)
68
  if lines is not None:
69
  angles = [np.arctan2(line[0][3] - line[0][1], line[0][2] - line[0][0]) * 180 / np.pi for line in lines]
70
  angle = np.median(angles)
71
+ if abs(angle) > 0.5:
72
  h, w = img.shape[:2]
73
  center = (w // 2, h // 2)
74
  M = cv2.getRotationMatrix2D(center, angle, 1.0)
 
81
  return img
82
 
83
  def detect_roi(img):
84
+ """Detect region of interest with relaxed contour analysis."""
85
  try:
86
  save_debug_image(img, "04_original")
87
  thresh, enhanced = preprocess_image(img)
88
  brightness_map = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
89
+ block_sizes = [max(11, min(41, int(img.shape[0] / s) * 2 + 1)) for s in [5, 10, 15]]
90
  valid_contours = []
91
  img_area = img.shape[0] * img.shape[1]
92
 
93
  for block_size in block_sizes:
94
  temp_thresh = cv2.adaptiveThreshold(
95
  enhanced, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
96
+ cv2.THRESH_BINARY_INV, block_size, 5
97
  )
98
+ kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
99
  temp_thresh = cv2.morphologyEx(temp_thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
100
  save_debug_image(temp_thresh, f"05_roi_threshold_block{block_size}")
101
  contours, _ = cv2.findContours(temp_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
105
  x, y, w, h = cv2.boundingRect(c)
106
  roi_brightness = np.mean(brightness_map[y:y+h, x:x+w])
107
  aspect_ratio = w / h
108
+ if (30 < area < (img_area * 0.98) and
109
+ 0.02 <= aspect_ratio <= 25.0 and w > 15 and h > 5 and roi_brightness > 10):
110
  valid_contours.append((c, area * roi_brightness))
111
  logging.debug(f"Contour (block {block_size}): Area={area}, Aspect={aspect_ratio:.2f}, Brightness={roi_brightness:.2f}")
112
 
113
  if valid_contours:
114
  contour, _ = max(valid_contours, key=lambda x: x[1])
115
  x, y, w, h = cv2.boundingRect(contour)
116
+ padding = max(5, min(25, int(min(w, h) * 0.5)))
117
  x, y = max(0, x - padding), max(0, y - padding)
118
  w, h = min(w + 2 * padding, img.shape[1] - x), min(h + 2 * padding, img.shape[0] - y)
119
  roi_img = img[y:y+h, x:x+w]
 
130
  return img, None
131
 
132
  def detect_digit_template(digit_img, brightness):
133
+ """Digit recognition with adjusted template matching."""
134
  try:
135
  h, w = digit_img.shape
136
  if h < 5 or w < 2:
137
  logging.debug("Digit image too small for template matching.")
138
  return None
139
 
 
140
  digit_templates = {
141
+ '0': [np.array([[1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]], dtype=np.float32)],
142
+ '1': [np.array([[0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0]], dtype=np.float32)],
143
+ '2': [np.array([[1, 1, 1, 1, 1], [0, 0, 0, 1, 1], [1, 1, 1, 1, 1], [1, 1, 0, 0, 0], [1, 1, 1, 1, 1]], dtype=np.float32)],
144
+ '3': [np.array([[1, 1, 1, 1, 1], [0, 0, 0, 1, 1], [1, 1, 1, 1, 1], [0, 0, 0, 1, 1], [1, 1, 1, 1, 1]], dtype=np.float32)],
145
+ '4': [np.array([[1, 1, 0, 0, 1], [1, 1, 0, 0, 1], [1, 1, 1, 1, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1]], dtype=np.float32)],
146
+ '5': [np.array([[1, 1, 1, 1, 1], [1, 1, 0, 0, 0], [1, 1, 1, 1, 1], [0, 0, 0, 1, 1], [1, 1, 1, 1, 1]], dtype=np.float32)],
147
+ '6': [np.array([[1, 1, 1, 1, 1], [1, 1, 0, 0, 0], [1, 1, 1, 1, 1], [1, 0, 0, 1, 1], [1, 1, 1, 1, 1]], dtype=np.float32)],
148
+ '7': [np.array([[1, 1, 1, 1, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1]], dtype=np.float32)],
149
+ '8': [np.array([[1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]], dtype=np.float32)],
150
+ '9': [np.array([[1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 1, 1, 1, 1], [0, 0, 0, 1, 1], [1, 1, 1, 1, 1]], dtype=np.float32)],
151
+ '.': [np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.float32)]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
152
  }
153
 
 
154
  sizes = [(5, 5), (4, 4), (3, 3)] if h > w else [(3, 3), (2, 2)]
155
  best_match, best_score = None, -1
156
  for size in sizes:
157
  digit_img_resized = cv2.resize(digit_img, size, interpolation=cv2.INTER_AREA)
158
+ digit_img_resized = (digit_img_resized > 90).astype(np.float32) # Adjusted binarization threshold
159
 
160
  for digit, templates in digit_templates.items():
161
  for template in templates:
 
 
 
 
162
  if template.shape[0] != size[0] or template.shape[1] != size[1]:
163
  continue
164
  result = cv2.matchTemplate(digit_img_resized, template, cv2.TM_CCOEFF_NORMED)
165
  _, max_val, _, _ = cv2.minMaxLoc(result)
166
+ if max_val > 0.50 and max_val > best_score: # Lowered threshold
167
  best_score = max_val
168
  best_match = digit
169
  logging.debug(f"Template match: {best_match}, Score: {best_score:.2f}")
170
+ return best_match if best_score > 0.50 else None
171
  except Exception as e:
172
  logging.error(f"Template digit detection failed: {str(e)}")
173
  return None
174
 
175
  def perform_ocr(img, roi_bbox):
176
+ """Perform OCR with enhanced Tesseract and template fallback."""
177
  try:
178
  thresh, enhanced = preprocess_image(img)
179
  brightness = estimate_brightness(img)
180
  pil_img = Image.fromarray(enhanced)
181
  save_debug_image(pil_img, "07_ocr_input")
182
 
183
+ # Enhanced Tesseract configurations
184
  configs = [
185
  r'--oem 3 --psm 7 -c tessedit_char_whitelist=0123456789.', # Single line
186
+ r'--oem 3 --psm 6 -c tessedit_char_whitelist=0123456789.', # Block of text
187
+ r'--oem 3 --psm 10 -c tessedit_char_whitelist=0123456789.' # Single character
188
  ]
189
  for config in configs:
190
  text = pytesseract.image_to_string(pil_img, config=config)
 
199
  logging.info(f"Validated Tesseract text: {text}, Confidence: {confidence:.2f}%")
200
  return text, confidence
201
 
202
+ # Enhanced template-based detection
203
  logging.info("Tesseract failed, using template-based detection.")
204
  contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
205
  digits_info = []
206
  for c in contours:
207
  x, y, w, h = cv2.boundingRect(c)
208
+ if w > 3 and h > 4 and 0.02 <= w/h <= 5.0:
209
  digits_info.append((x, x+w, y, y+h))
210
 
211
  if digits_info:
 
222
  digit = detect_digit_template(digit_crop, brightness)
223
  if digit:
224
  recognized_text += digit
225
+ elif x_min - prev_x_max < 15 and prev_x_max != -float('inf'):
226
  recognized_text += '.'
227
  prev_x_max = x_max
228
 
 
243
  return None, 0.0
244
 
245
  def extract_weight_from_image(pil_img):
246
+ """Extract weight from any digital scale image with adjusted thresholds."""
247
  try:
248
  img = np.array(pil_img)
249
  img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
250
  save_debug_image(img, "00_input_image")
251
  img = correct_rotation(img)
252
  brightness = estimate_brightness(img)
253
+ conf_threshold = 0.60 if brightness > 70 else 0.40 # Lowered threshold
254
 
255
  # Try ROI-based detection
256
  roi_img, roi_bbox = detect_roi(img)
257
  if roi_bbox:
258
+ conf_threshold *= 1.2 if (roi_bbox[2] * roi_bbox[3]) > (img.shape[0] * img.shape[1] * 0.03) else 1.0
259
 
260
  result, confidence = perform_ocr(roi_img, roi_bbox)
261
  if result and confidence >= conf_threshold * 100:
 
268
  except ValueError:
269
  logging.warning(f"Invalid weight format: {result}")
270
 
271
+ # Full image fallback with relaxed threshold
272
  logging.info("Primary OCR failed, using full image fallback.")
273
  result, confidence = perform_ocr(img, None)
274
+ if result and confidence >= conf_threshold * 0.80 * 100:
275
  try:
276
  weight = float(result)
277
  if 0.001 <= weight <= 5000: