{"id":87777,"date":"2020-07-10T01:42:13","date_gmt":"2020-07-09T23:42:13","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/novaya-sistema-opticheskogo-raspoznavaniya-teksta-easyocr"},"modified":"2020-07-10T01:42:13","modified_gmt":"2020-07-09T23:42:13","slug":"novaya-sistema-opticheskogo-raspoznavaniya-teksta-easyocr","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/news\/novaya-sistema-opticheskogo-raspoznavaniya-teksta-easyocr","title":{"rendered":"Noua sistem\u0103 de recunoa\u0219tere optic\u0103 a textului EasyOCR","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Proiectul EasyOCR<\/a><\/noindex> se dezvolt\u0103 un nou sistem de recunoa\u0219tere optic\u0103 a textului, care suport\u0103 peste 40 de limbi, inclusiv englez\u0103, german\u0103, francez\u0103, japonez\u0103, chinez\u0103, coreean\u0103, uzbec\u0103, azer\u0103 \u0219i lituanian\u0103. Limbile bazate pe chirilic\u0103 nu sunt \u00eenc\u0103 suportate, dar vor fi ad\u0103ugate \u00een lista de planuri. Codul este scris \u00een Python folosind cadrul PyTorch<\/a><\/noindex> \u0219i se r\u0103sp\u00e2nde\u0219te<\/a><\/noindex> sub licen\u021ba Apache 2.0. Pentru desc\u0103rcare ofer\u0103<\/a><\/noindex> modele preg\u0103tite pentru limbile bazate pe alfabetul latin \u0219i hieroglife.<\/p>\n

Pentru determinarea \u0219i recunoa\u0219terea textului din imagine, se aplic\u0103 metode de \u00eenv\u0103\u021bare automat\u0103. Algoritmul de \u00eenv\u0103\u021bare automat\u0103 utilizat pentru determinarea textului este CRAFT<\/a><\/noindex> (Character-Region Awareness For Text) \u00een implementare<\/a><\/noindex> pentru PyTorch, capabil s\u0103 extrag\u0103 textul de pe obiecte diverse, inclusiv etichete, pl\u0103ci informative \u0219i semne de circula\u021bie. Pentru recunoa\u0219terea secven\u021belor de caractere se folose\u0219te o re\u021bea neural\u0103 convolu\u021bional-recurent\u0103 CRNN<\/a><\/noindex> (Convolutional Recurrent Neural Network, combina\u021bia dintre DCNN \u0219i RNN) \u0219i algoritmul CTC BeamSearch<\/a><\/noindex> CTC BeamSearch (Connectionist Temporal Classification) pentru decodarea ie\u0219irilor neuronale \u00een reprezent\u0103ri textuale. <\/p>\n

<\/a><\/noindex><\/p>\n

Sursa: opennet.ro<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041f\u0440\u043e\u0435\u043a\u0442\u043e\u043c EasyOCR \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u043d\u043e\u0432\u0430\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u043e\u043f\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0433\u043e \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u0442\u0435\u043a\u0441\u0442\u0430, \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u044e\u0449\u0430\u044f \u0431\u043e\u043b\u0435\u0435 40 \u044f\u0437\u044b\u043a\u043e\u0432, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u0430\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439, \u043d\u0435\u043c\u0435\u0446\u043a\u0438\u0439, \u0444\u0440\u0430\u043d\u0446\u0443\u0437\u0441\u043a\u0438\u0439, \u044f\u043f\u043e\u043d\u0441\u043a\u0438\u0439, \u043a\u0438\u0442\u0430\u0439\u0441\u043a\u0438\u0439, \u043a\u043e\u0440\u0435\u0439\u0441\u043a\u0438\u0439, \u0443\u0437\u0431\u0435\u043a\u0441\u043a\u0438\u0439, 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