
{"id":27162,"date":"2024-02-29T11:45:18","date_gmt":"2024-02-29T12:45:18","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%a8%d9%87%d8%a8%d9%88%d8%af-%da%a9%db%8c%d9%81%db%8c%d8%aa-ocr-%d8%af%d8%b1-%d8%a7%d8%b3%d9%86%d8%a7%d8%af-%d8%aa%d8%a7%d8%b1%db%8c%d8%ae%db%8c-%d9%82%d8%b1%d9%86\/"},"modified":"2024-02-29T11:45:30","modified_gmt":"2024-02-29T12:45:30","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%a8%d9%87%d8%a8%d9%88%d8%af-%da%a9%db%8c%d9%81%db%8c%d8%aa-ocr-%d8%af%d8%b1-%d8%a7%d8%b3%d9%86%d8%a7%d8%af-%d8%aa%d8%a7%d8%b1%db%8c%d8%ae%db%8c-%d9%82%d8%b1%d9%86","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%a8%d9%87%d8%a8%d9%88%d8%af-%da%a9%db%8c%d9%81%db%8c%d8%aa-ocr-%d8%af%d8%b1-%d8%a7%d8%b3%d9%86%d8%a7%d8%af-%d8%aa%d8%a7%d8%b1%db%8c%d8%ae%db%8c-%d9%82%d8%b1%d9%86\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 \u0628\u0647\u0628\u0648\u062f \u06a9\u06cc\u0641\u06cc\u062a OCR \u062f\u0631 \u0627\u0633\u0646\u0627\u062f \u062a\u0627\u0631\u06cc\u062e\u06cc \u0642\u0631\u0646 \u0646\u0648\u0632\u062f\u0647\u0645 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u062a\u0631\u06a9\u06cc\u0628\u06cc"},"content":{"rendered":"<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach<\/td>\n<\/tr>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n<td>\u0645\u0642\u0627\u0644\u0647 \u0628\u0647\u0628\u0648\u062f \u06a9\u06cc\u0641\u06cc\u062a OCR \u062f\u0631 \u0627\u0633\u0646\u0627\u062f \u062a\u0627\u0631\u06cc\u062e\u06cc \u0642\u0631\u0646 \u0646\u0648\u0632\u062f\u0647\u0645 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u062a\u0631\u06a9\u06cc\u0628\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n<td>David Fleischhacker, Wolfgang Goederle, Roman Kern<\/td>\n<\/tr>\n<tr>\n<td>\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n<td>\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\n<td>PDF<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n<td>29<\/td>\n<\/tr>\n<tr>\n<td>\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a  <\/td>\n<td>Computer Vision and Pattern Recognition,Machine Learning,\u0686\u0634\u0645 \u0627\u0646\u062f\u0627\u0632 \u0631\u0627\u06cc\u0627\u0646\u0647 \u0648 \u062a\u0634\u062e\u06cc\u0635 \u0627\u0644\u06af\u0648\u06cc , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 ,<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a    <\/td>\n<td>Submitted 15 January, 2024; originally announced January 2024. , Comments: 29 pages, 23 figures, 7 tables<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc    <\/td>\n<td>\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u062f\u0631 15 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: 29 \u0635\u0641\u062d\u0647 \u060c 23 \u0634\u06a9\u0644 \u060c 7 \u062c\u062f\u0648\u0644<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n<p style=\"direction:ltr;\">This paper addresses a major challenge to historical research on the 19th century. Large quantities of sources have become digitally available for the first time, while extraction techniques are lagging behind. Therefore, we researched machine learning (ML) models to recognise and extract complex data structures in a high-value historical primary source, the Schematismus. It records every single person in the Habsburg civil service above a certain hierarchical level between 1702 and 1918 and documents the genesis of the central administration over two centuries. Its complex and intricate structure as well as its enormous size have so far made any more comprehensive analysis of the administrative and social structure of the later Habsburg Empire on the basis of this source impossible. We pursued two central objectives: Primarily, the improvement of the OCR quality, for which we considered an improved structure recognition to be essential; in the further course, it turned out that this also made the extraction of the data structure possible. We chose Faster R-CNN as base for the ML architecture for structure recognition. In order to obtain the required amount of training data quickly and economically, we synthesised Hof- und Staatsschematismus-style data, which we used to train our model. The model was then fine-tuned with a smaller set of manually annotated historical source data. We then used Tesseract-OCR, which was further optimised for the style of our documents, to complete the combined structure extraction and OCR process. Results show a significant decrease in the two standard parameters of OCR-performance, WER and CER (where lower values are better). Combined structure detection and fine-tuned OCR improved CER and WER values by remarkable 71.98 percent (CER) respectively 52.49 percent (WER).<\/p>\n<h2>\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u06cc\u06a9 \u0686\u0627\u0644\u0634 \u0628\u0632\u0631\u06af \u0628\u0631\u0627\u06cc \u062a\u062d\u0642\u06cc\u0642\u0627\u062a \u062a\u0627\u0631\u06cc\u062e\u06cc \u062f\u0631 \u0642\u0631\u0646 \u0646\u0648\u0632\u062f\u0647\u0645 \u0645\u06cc \u067e\u0631\u062f\u0627\u0632\u06cc\u0645.\u0645\u0642\u0627\u062f\u06cc\u0631 \u0632\u06cc\u0627\u062f\u06cc \u0627\u0632 \u0645\u0646\u0627\u0628\u0639 \u0628\u0631\u0627\u06cc \u0627\u0648\u0644\u06cc\u0646 \u0628\u0627\u0631 \u0628\u0647 \u0635\u0648\u0631\u062a \u062f\u06cc\u062c\u06cc\u062a\u0627\u0644\u06cc \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a\u0647 \u0627\u0646\u062f \u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0639\u0642\u0628 \u0645\u0627\u0646\u062f\u0647 \u0627\u0646\u062f.\u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u060c \u0645\u0627 \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 (ML) \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0634\u062e\u06cc\u0635 \u0648 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0633\u0627\u062e\u062a\u0627\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u067e\u06cc\u0686\u06cc\u062f\u0647 \u062f\u0631 \u06cc\u06a9 \u0645\u0646\u0628\u0639 \u0627\u0648\u0644\u06cc\u0647 \u062a\u0627\u0631\u06cc\u062e\u06cc \u0628\u0627 \u0627\u0631\u0632\u0634 \u0628\u0627\u0644\u0627 \u060c \u0634\u0645\u0627\u062a\u06cc\u0633\u0645 \u062a\u062d\u0642\u06cc\u0642 \u06a9\u0631\u062f\u06cc\u0645.\u0627\u06cc\u0646 \u0641\u0631\u062f \u062a\u06a9 \u0634\u062e\u0635 \u0631\u0627 \u062f\u0631 \u062e\u062f\u0645\u0627\u062a \u0645\u0644\u06a9\u06cc \u0647\u0627\u0628\u0633\u0628\u0648\u0631\u06af \u0628\u0627\u0644\u0627\u062a\u0631 \u0627\u0632 \u0633\u0637\u062d \u0633\u0644\u0633\u0644\u0647 \u0645\u0631\u0627\u062a\u0628\u06cc \u062e\u0627\u0635 \u0628\u06cc\u0646 \u0633\u0627\u0644\u0647\u0627\u06cc 1702 \u062a\u0627 1918 \u062b\u0628\u062a \u0645\u06cc \u06a9\u0646\u062f \u0648 \u067e\u06cc\u062f\u0627\u06cc\u0634 \u062f\u0648\u0644\u062a \u0645\u0631\u06a9\u0632\u06cc \u0631\u0627 \u0637\u06cc \u062f\u0648 \u0642\u0631\u0646 \u0645\u0633\u062a\u0646\u062f \u0645\u06cc \u06a9\u0646\u062f.\u0633\u0627\u062e\u062a\u0627\u0631 \u067e\u06cc\u0686\u06cc\u062f\u0647 \u0648 \u067e\u06cc\u0686\u06cc\u062f\u0647 \u0622\u0646 \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0627\u0646\u062f\u0627\u0632\u0647 \u0639\u0638\u06cc\u0645 \u0622\u0646 \u062a\u0627\u06a9\u0646\u0648\u0646 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u062c\u0627\u0645\u0639 \u062a\u0631\u06cc \u0627\u0632 \u0633\u0627\u062e\u062a\u0627\u0631 \u0627\u062f\u0627\u0631\u06cc \u0648 \u0627\u062c\u062a\u0645\u0627\u0639\u06cc \u0627\u0645\u067e\u0631\u0627\u062a\u0648\u0631\u06cc \u0628\u0639\u062f\u06cc \u0647\u0627\u0628\u0633\u0628\u0648\u0631\u06af \u0628\u0631 \u0627\u0633\u0627\u0633 \u0627\u06cc\u0646 \u0645\u0646\u0628\u0639 \u063a\u06cc\u0631\u0645\u0645\u06a9\u0646 \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f\u0647 \u0627\u0633\u062a.\u0645\u0627 \u062f\u0648 \u0647\u062f\u0641 \u0627\u0635\u0644\u06cc \u0631\u0627 \u062f\u0646\u0628\u0627\u0644 \u06a9\u0631\u062f\u06cc\u0645: \u062f\u0631 \u062f\u0631\u062c\u0647 \u0627\u0648\u0644 \u060c \u0628\u0647\u0628\u0648\u062f \u06a9\u06cc\u0641\u06cc\u062a OCR \u060c \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0622\u0646 \u0645\u0627 \u06cc\u06a9 \u0633\u0627\u062e\u062a\u0627\u0631 \u0628\u0647\u0628\u0648\u062f \u06cc\u0627\u0641\u062a\u0647 \u0631\u0627 \u0636\u0631\u0648\u0631\u06cc \u062f\u0627\u0646\u0633\u062a\u06cc\u0645.\u062f\u0631 \u062f\u0648\u0631\u0647 \u0628\u0639\u062f\u06cc \u060c \u0645\u0639\u0644\u0648\u0645 \u0634\u062f \u06a9\u0647 \u0627\u06cc\u0646 \u0627\u0645\u0631 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0627\u0639\u062b \u0634\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0633\u0627\u062e\u062a\u0627\u0631 \u062f\u0627\u062f\u0647 \u0645\u0645\u06a9\u0646 \u0628\u0627\u0634\u062f.\u0645\u0627 R-CNN \u0633\u0631\u06cc\u0639\u062a\u0631 \u0631\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u067e\u0627\u06cc\u0647 \u0628\u0631\u0627\u06cc \u0645\u0639\u0645\u0627\u0631\u06cc ML \u0628\u0631\u0627\u06cc \u0634\u0646\u0627\u062e\u062a \u0633\u0627\u062e\u062a\u0627\u0631 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0631\u062f\u06cc\u0645.\u0628\u0647 \u0645\u0646\u0638\u0648\u0631 \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0646 \u0645\u0642\u062f\u0627\u0631 \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0628\u0647 \u0633\u0631\u0639\u062a \u0648 \u0627\u0642\u062a\u0635\u0627\u062f\u06cc \u060c \u0645\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc Hof- und Staatsschematismus \u0631\u0627 \u06a9\u0647 \u0627\u0632 \u0622\u0646 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u062e\u0648\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0631\u062f\u06cc\u0645 \u060c \u0633\u0646\u062a\u0632 \u06a9\u0631\u062f\u06cc\u0645.\u0633\u067e\u0633 \u0627\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u06cc \u06a9\u0648\u0686\u06a9\u062a\u0631 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0646\u0628\u0639 \u062a\u0627\u0631\u06cc\u062e\u06cc \u062d\u0627\u0634\u06cc\u0647 \u0646\u0648\u06cc\u0633\u06cc \u062f\u0633\u062a\u06cc \u062a\u0646\u0638\u06cc\u0645 \u0634\u062f.\u0645\u0627 \u0633\u067e\u0633 \u0627\u0632 Tesseract-OCR \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645 \u06a9\u0647 \u0628\u06cc\u0634\u062a\u0631 \u0628\u0631\u0627\u06cc \u0633\u0628\u06a9 \u0627\u0633\u0646\u0627\u062f \u0645\u0627 \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u06cc \u0634\u062f \u062a\u0627 \u0631\u0648\u0646\u062f \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0633\u0627\u062e\u062a\u0627\u0631 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0648 \u0641\u0631\u0622\u06cc\u0646\u062f OCR \u0631\u0627 \u062a\u06a9\u0645\u06cc\u0644 \u06a9\u0646\u06cc\u0645.\u0646\u062a\u0627\u06cc\u062c \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u06a9\u0627\u0647\u0634 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u062f\u0631 \u062f\u0648 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0639\u0645\u0644\u06a9\u0631\u062f OCR \u060c WER \u0648 CER (\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u0627\u06cc\u06cc\u0646 \u0628\u0647\u062a\u0631 \u0647\u0633\u062a\u0646\u062f) \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f.\u062a\u0634\u062e\u06cc\u0635 \u0633\u0627\u062e\u062a\u0627\u0631 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0648 OCR \u0628\u0627 \u062a\u0646\u0638\u06cc\u0645 \u062f\u0642\u06cc\u0642 \u0645\u0642\u0627\u062f\u06cc\u0631 CER \u0648 WER \u0631\u0627 \u0628\u0627 71.98 \u062f\u0631\u0635\u062f \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647 (CER) \u0628\u0647 \u062a\u0631\u062a\u06cc\u0628 52.49 \u062f\u0631\u0635\u062f (WER) \u0628\u0647\u0628\u0648\u062f \u0628\u062e\u0634\u06cc\u062f.<\/p>\n<table class=\"table table-bordered table-primary\">\r\n<tr>\r\n\t<td>\r\n\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u0627\u0633\u062a.\r\n\t<\/td>\r\n<\/tr>\r\n\t\r\n<tr>\r\n\t<td>\r\n     \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0648\u0634 \u0647\u0627\u06cc \u062a\u0645\u0627\u0633\u060c \u067e\u06cc\u0627\u0645\u06a9\u060c \u062a\u0644\u06af\u0631\u0627\u0645 \u0648 \u06cc\u0627 \u0648\u0627\u062a\u0633 \u0627\u067e \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 \u0632\u06cc\u0631 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f:\r\n\t<br \/><br \/>\r\n\t09395106248\r\n\t\t\r\n\t\t\r\n\t<br \/><br \/>\r\n\t\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0634\u0631\u0627\u06cc\u0637 \u062a\u0631\u062c\u0645\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0627\u0633\u062a:\r\n\t\t<br \/>\r\n\t\t<ul>\r\n\t\t<li>\r\n\t\t\u0642\u06cc\u0645\u062a \u0647\u0631 \u0635\u0641\u062d\u0647 \u062a\u0631\u062c\u0645\u0647 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 40 \u0647\u0632\u0627\u0631 \u062a\u0648\u0645\u0627\u0646 \u0645\u06cc \u0628\u0627\u0634\u062f. \r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u062a\u062d\u0648\u06cc\u0644 \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f \u0645\u06cc \u0628\u0627\u0634\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0635\u0648\u0631\u062a \u062f\u0627\u0634\u062a\u0646 \u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0639\u0627\u062f\u06cc \u0628\u06cc\u0646 3 \u062a\u0627 5 \u0631\u0648\u0632 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t\r\n\t\t<li>\r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.\r\n\t\t<\/li>\r\n\t<\/td>\r\n\t\r\n\t\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach \u0639\u0646\u0648\u0627\u0646 [&hellip;]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"closed","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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