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Feat/temperature scaling confidence calibration #1434

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82fd085
Update recognition.py
David-Magdy Sep 3, 2025
496288e
Update easyocr.py
David-Magdy Sep 3, 2025
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6 changes: 3 additions & 3 deletions easyocr/easyocr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -355,7 +355,7 @@ def recognize(self, img_cv_grey, horizontal_list=None, free_list=None,\
workers = 0, allowlist = None, blocklist = None, detail = 1,\
rotation_info = None,paragraph = False,\
contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\
y_ths = 0.5, x_ths = 1.0, reformat=True, output_format='standard'):
y_ths = 0.5, x_ths = 1.0, reformat=True, output_format='standard',temperature=1.0):

if reformat:
img, img_cv_grey = reformat_input(img_cv_grey)
Expand Down Expand Up @@ -383,15 +383,15 @@ def recognize(self, img_cv_grey, horizontal_list=None, free_list=None,\
image_list, max_width = get_image_list(h_list, f_list, img_cv_grey, model_height = imgH)
result0 = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list,\
ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths,\
workers, self.device)
workers, self.device,temperature)
result += result0
for bbox in free_list:
h_list = []
f_list = [bbox]
image_list, max_width = get_image_list(h_list, f_list, img_cv_grey, model_height = imgH)
result0 = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list,\
ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths,\
workers, self.device)
workers, self.device,temperature)
result += result0
# default mode will try to process multiple boxes at the same time
else:
Expand Down
17 changes: 12 additions & 5 deletions easyocr/recognition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,7 +97,7 @@ def __call__(self, batch):
return image_tensors

def recognizer_predict(model, converter, test_loader, batch_max_length,\
ignore_idx, char_group_idx, decoder = 'greedy', beamWidth= 5, device = 'cpu'):
ignore_idx, char_group_idx, decoder = 'greedy', beamWidth= 5, device = 'cpu', temperature=1.0):
model.eval()
result = []
with torch.no_grad():
Expand All @@ -110,6 +110,9 @@ def recognizer_predict(model, converter, test_loader, batch_max_length,\

preds = model(image, text_for_pred)

# Apply Temperature Scaling
preds = preds / temperature

# Select max probabilty (greedy decoding) then decode index to character
preds_size = torch.IntTensor([preds.size(1)] * batch_size)

Expand Down Expand Up @@ -145,7 +148,11 @@ def recognizer_predict(model, converter, test_loader, batch_max_length,\
preds_max_prob.append(np.array([0]))

for pred, pred_max_prob in zip(preds_str, preds_max_prob):
confidence_score = custom_mean(pred_max_prob)
# Replaced custom_mean with a standard average of probabilities
if len(pred_max_prob) == 0:
confidence_score = 0.0
else:
confidence_score = pred_max_prob.mean()
result.append([pred, confidence_score])

return result
Expand Down Expand Up @@ -185,7 +192,7 @@ def get_recognizer(recog_network, network_params, character,\

def get_text(character, imgH, imgW, recognizer, converter, image_list,\
ignore_char = '',decoder = 'greedy', beamWidth =5, batch_size=1, contrast_ths=0.1,\
adjust_contrast=0.5, filter_ths = 0.003, workers = 1, device = 'cpu'):
adjust_contrast=0.5, filter_ths = 0.003, workers = 1, device = 'cpu', temperature=1.0):
batch_max_length = int(imgW/10)

char_group_idx = {}
Expand All @@ -204,7 +211,7 @@ def get_text(character, imgH, imgW, recognizer, converter, image_list,\

# predict first round
result1 = recognizer_predict(recognizer, converter, test_loader,batch_max_length,\
ignore_idx, char_group_idx, decoder, beamWidth, device = device)
ignore_idx, char_group_idx, decoder, beamWidth, device=device, temperature=temperature)

# predict second round
low_confident_idx = [i for i,item in enumerate(result1) if (item[1] < contrast_ths)]
Expand All @@ -216,7 +223,7 @@ def get_text(character, imgH, imgW, recognizer, converter, image_list,\
test_data, batch_size=batch_size, shuffle=False,
num_workers=int(workers), collate_fn=AlignCollate_contrast, pin_memory=True)
result2 = recognizer_predict(recognizer, converter, test_loader, batch_max_length,\
ignore_idx, char_group_idx, decoder, beamWidth, device = device)
ignore_idx, char_group_idx, decoder, beamWidth, device=device, temperature=temperature)

result = []
for i, zipped in enumerate(zip(coord, result1)):
Expand Down