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Parametrize OCR for handling curved lines

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vahidrezanezhad 2025-05-31 01:09:14 +02:00
parent 48285ce3f5
commit 928a548b70
2 changed files with 6 additions and 6 deletions
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8
src/eynollah/eynollah.py
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@ -5353,7 +5353,7 @@ class Eynollah_ocr:
self.b_s = int(batch_size)
else:
self.model_ocr_dir = dir_models + "/model_step_1150000_ocr"#"/model_step_125000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
self.model_ocr_dir = dir_models + "/model_step_1225000_ocr"#"/model_step_125000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
model_ocr = load_model(self.model_ocr_dir , compile=False)
self.prediction_model = tf.keras.models.Model(
@ -5684,7 +5684,7 @@ class Eynollah_ocr:
img_crop_bin = img_crop_bin[y_n:y_n+h_n, x_n:x_n+w_n, :]
img_crop_bin[mask_poly==0] = 255
if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 100:
if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 90:
if self.prediction_with_both_of_rgb_and_bin:
img_crop, img_crop_bin = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly, img_crop_bin)
else:
@ -5698,7 +5698,7 @@ class Eynollah_ocr:
if type_textregion=='drop-capital':
pass
else:
if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 100:
if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 90:
if self.prediction_with_both_of_rgb_and_bin:
img_crop, img_crop_bin = break_curved_line_into_small_pieces_and_then_merge(img_crop, mask_poly, img_crop_bin)
else:
@ -5708,7 +5708,7 @@ class Eynollah_ocr:
if not self.export_textline_images_and_text:
if w_scaled < 640:#1.5*image_width:
if w_scaled < 530:#640:#1.5*image_width:
img_fin = preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin)
if angle_degrees > 15:

2
src/eynollah/utils/utils_ocr.py
View file

@ -241,7 +241,7 @@ def return_splitting_point_of_image(image_to_spliited):
peaks_real = peaks_real[(peaks_real<width2) & (peaks_real>width1)]
arg_sort = np.argsort(sum_smoothed[peaks_real])
peaks_sort_4 = peaks_real[arg_sort][::-1][:4]
peaks_sort_4 = peaks_real[arg_sort][::-1][:3]
return np.sort(peaks_sort_4)