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https://github.com/qurator-spk/eynollah.git
synced 2025-08-29 11:59:55 +02:00
Implement hyphenated textline merging in OCR engine and a bug fixed for curved textline OCR
This commit is contained in:
vahidrezanezhad
parent
c0835665a9
commit
f94fc9973b
1 changed files with 71 additions and 86 deletions
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@ -5500,7 +5500,6 @@ class Eynollah_ocr:
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def get_orientation_moments_of_mask(self, mask):
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def get_orientation_moments_of_mask(self, mask):
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mask=mask.astype('uint8')
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mask=mask.astype('uint8')
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print(mask.shape)
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contours, _ = cv2.findContours(mask[:,:,0], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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contours, _ = cv2.findContours(mask[:,:,0], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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largest_contour = max(contours, key=cv2.contourArea) if contours else None
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largest_contour = max(contours, key=cv2.contourArea) if contours else None
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@ -5547,97 +5546,69 @@ class Eynollah_ocr:
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def break_curved_line_into_small_pieces_and_then_merge(self, img_curved, mask_curved):
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def break_curved_line_into_small_pieces_and_then_merge(self, img_curved, mask_curved):
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peaks_4 = self.return_splitting_point_of_image(img_curved)
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peaks_4 = self.return_splitting_point_of_image(img_curved)
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if len(peaks_4)>0:
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imgs_tot = []
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for ind in range(len(peaks_4)+1):
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if ind==0:
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img = img_curved[:, :peaks_4[ind], :]
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mask = mask_curved[:, :peaks_4[ind], :]
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elif ind==len(peaks_4):
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img = img_curved[:, peaks_4[ind-1]:, :]
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mask = mask_curved[:, peaks_4[ind-1]:, :]
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else:
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img = img_curved[:, peaks_4[ind-1]:peaks_4[ind], :]
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mask = mask_curved[:, peaks_4[ind-1]:peaks_4[ind], :]
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or_ma = self.get_orientation_moments_of_mask(mask)
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imgs_tot.append([img, mask, or_ma] )
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w_tot_des_list = []
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w_tot_des = 0
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imgs_deskewed_list = []
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for ind in range(len(imgs_tot)):
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img_in = imgs_tot[ind][0]
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mask_in = imgs_tot[ind][1]
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ori_in = imgs_tot[ind][2]
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img_0 = img_curved[:, :peaks_4[0], :]
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if abs(ori_in)<45:
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img_1 = img_curved[:, peaks_4[0]:peaks_4[1], :]
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img_in_des = self.rotate_image_with_padding(img_in, ori_in, border_value=(255,255,255) )
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img_2 = img_curved[:, peaks_4[1]:peaks_4[2], :]
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mask_in_des = self.rotate_image_with_padding(mask_in, ori_in)
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img_3 = img_curved[:, peaks_4[2]:peaks_4[3], :]
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mask_in_des = mask_in_des.astype('uint8')
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img_4 = img_curved[:, peaks_4[3]:, :]
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#new bounding box
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x_n, y_n, w_n, h_n = self.get_contours_and_bounding_boxes(mask_in_des[:,:,0])
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mask_in_des = mask_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
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img_in_des = img_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
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w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
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img_in_des = resize_image(img_in_des, 32, w_relative)
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mask_0 = mask_curved[:, :peaks_4[0], :]
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else:
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mask_1 = mask_curved[:, peaks_4[0]:peaks_4[1], :]
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img_in_des = np.copy(img_in)
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mask_2 = mask_curved[:, peaks_4[1]:peaks_4[2], :]
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w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
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mask_3 = mask_curved[:, peaks_4[2]:peaks_4[3], :]
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img_in_des = resize_image(img_in_des, 32, w_relative)
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mask_4 = mask_curved[:, peaks_4[3]:, :]
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cv2.imwrite("split0.png", img_0)
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w_tot_des+=img_in_des.shape[1]
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cv2.imwrite("split1.png", img_1)
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w_tot_des_list.append(img_in_des.shape[1])
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cv2.imwrite("split2.png", img_2)
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imgs_deskewed_list.append(img_in_des)
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cv2.imwrite("split3.png", img_3)
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or_ma_0 = self.get_orientation_moments_of_mask(mask_0)
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or_ma_1 = self.get_orientation_moments_of_mask(mask_1)
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or_ma_2 = self.get_orientation_moments_of_mask(mask_2)
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or_ma_3 = self.get_orientation_moments_of_mask(mask_3)
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or_ma_4 = self.get_orientation_moments_of_mask(mask_4)
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imgs_tot = []
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imgs_tot.append([img_0, mask_0, or_ma_0] )
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imgs_tot.append([img_1, mask_1, or_ma_1])
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imgs_tot.append([img_2, mask_2, or_ma_2])
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imgs_tot.append([img_3, mask_3, or_ma_3])
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imgs_tot.append([img_4, mask_4, or_ma_4])
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w_tot_des_list = []
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w_tot_des = 0
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imgs_deskewed_list = []
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for ind in range(len(imgs_tot)):
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img_in = imgs_tot[ind][0]
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mask_in = imgs_tot[ind][1]
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ori_in = imgs_tot[ind][2]
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if abs(ori_in)<45:
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img_in_des = self.rotate_image_with_padding(img_in, ori_in, border_value=(255,255,255) )
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mask_in_des = self.rotate_image_with_padding(mask_in, ori_in)
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mask_in_des = mask_in_des.astype('uint8')
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#new bounding box
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x_n, y_n, w_n, h_n = self.get_contours_and_bounding_boxes(mask_in_des[:,:,0])
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mask_in_des = mask_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
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img_in_des = img_in_des[y_n:y_n+h_n, x_n:x_n+w_n, :]
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w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
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img_in_des = resize_image(img_in_des, 32, w_relative)
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else:
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img_in_des = np.copy(img_in)
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w_relative = int(32 * img_in_des.shape[1]/float(img_in_des.shape[0]) )
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img_in_des = resize_image(img_in_des, 32, w_relative)
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w_tot_des+=img_in_des.shape[1]
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w_tot_des_list.append(img_in_des.shape[1])
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imgs_deskewed_list.append(img_in_des)
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img_final_deskewed = np.zeros((32, w_tot_des, 3))+255
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img_final_deskewed = np.zeros((32, w_tot_des, 3))+255
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w_indexer = 0
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w_indexer = 0
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for ind in range(len(w_tot_des_list)):
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for ind in range(len(w_tot_des_list)):
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img_final_deskewed[:,w_indexer:w_indexer+w_tot_des_list[ind],:] = imgs_deskewed_list[ind][:,:,:]
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img_final_deskewed[:,w_indexer:w_indexer+w_tot_des_list[ind],:] = imgs_deskewed_list[ind][:,:,:]
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w_indexer = w_indexer+w_tot_des_list[ind]
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w_indexer = w_indexer+w_tot_des_list[ind]
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return img_final_deskewed
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#cv2.imwrite('final.png', img_final_deskewed)
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else:
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#print(or_ma_0, or_ma_1, or_ma_2, or_ma_3, or_ma_4, 'orients')
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return img_curved
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##cv2.imwrite("split4.png", img_curved[:, peaks_4[3]:peaks_4[4], :])
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##cv2.imwrite("split5.png", img_curved[:, peaks_4[4]:peaks_4[5], :])
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##cv2.imwrite("split6.png", img_curved[:, peaks_4[5]:peaks_4[6], :])
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##cv2.imwrite("split7.png", img_curved[:, peaks_4[6]:peaks_4[7], :])
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##cv2.imwrite("split8.png", img_curved[:, peaks_4[7]:peaks_4[8], :])
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##cv2.imwrite("split9.png", img_curved[:, peaks_4[8]:peaks_4[9], :])
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#cv2.imwrite("split4.png", img_4)
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#sys.exit()
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return img_final_deskewed
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def run(self):
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def run(self):
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ls_imgs = os.listdir(self.dir_in)
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ls_imgs = os.listdir(self.dir_in)
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@ -6144,7 +6115,21 @@ class Eynollah_ocr:
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text_by_textregion = []
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text_by_textregion = []
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for ind in unique_cropped_lines_region_indexer:
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for ind in unique_cropped_lines_region_indexer:
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extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind]
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extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind]
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text_by_textregion.append("".join(extracted_texts_merged_un))
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if len(extracted_texts_merged_un)>1:
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text_by_textregion_ind = ""
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next_glue = ""
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for indt in range(len(extracted_texts_merged_un)):
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if extracted_texts_merged_un[indt].endswith('⸗') or extracted_texts_merged_un[indt].endswith('-'):
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text_by_textregion_ind = text_by_textregion_ind + next_glue + extracted_texts_merged_un[indt][:-1]
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next_glue = ""
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else:
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text_by_textregion_ind = text_by_textregion_ind + next_glue + extracted_texts_merged_un[indt]
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next_glue = " "
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text_by_textregion.append(text_by_textregion_ind)
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else:
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text_by_textregion.append(" ".join(extracted_texts_merged_un))
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#print(text_by_textregion, 'text_by_textregiontext_by_textregiontext_by_textregiontext_by_textregiontext_by_textregion')
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indexer = 0
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indexer = 0
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indexer_textregion = 0
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indexer_textregion = 0
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