{"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\/de\/blog\/news\/novaya-sistema-opticheskogo-raspoznavaniya-teksta-easyocr","title":{"rendered":"Das neue System zur optischen Zeichenerkennung EasyOCR","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Projekt EasyOCR<\/a><\/noindex> Es wird ein neues optisches Texterkennungssystem entwickelt, das \u00fcber 40 Sprachen unterst\u00fctzt, darunter Englisch, Deutsch, Franz\u00f6sisch, Japanisch, Chinesisch, Koreanisch, Usbekisch, Aserbaidschanisch und Litauisch. Sprachen, die auf dem kyrillischen Alphabet basieren, werden derzeit nicht unterst\u00fctzt, deren Hinzuf\u00fcgen steht jedoch auf der Liste der Pl\u00e4ne. Der Code ist in Python unter Verwendung des Frameworks PyTorch<\/a><\/noindex> und wird verbreitet<\/a><\/noindex> unter der Lizenz Apache 2.0. Um herunterzuladen werden<\/a><\/noindex> fertige Modelle f\u00fcr Sprachen, die auf dem lateinischen Alphabet und Schriftzeichen basieren.<\/p>\n

Zur Erkennung und Identifizierung von Text in Bildern werden Methoden des maschinellen Lernens eingesetzt. Zur Textbestimmung wird der Algorithmus CRAFT<\/a><\/noindex> (Character-Region Awareness For Text) in Implementierung.<\/a><\/noindex> f\u00fcr PyTorch verwendet, der in der Lage ist, Text auf beliebigen Objekten zu erkennen, einschlie\u00dflich Etiketten, Informationsschildern und Verkehrsschildern. Zur Erkennung von Zeichenfolgen wird ein konvolutional-recurrentes neuronales Netzwerk CRNN<\/a><\/noindex> (Convolutional Recurrent Neural Network, eine Kombination aus DCNN und RNN) und der Algorithmus CTC BeamSearch<\/a><\/noindex> CTC BeamSearch (Connectionist Temporal Classification) zur Dekodierung der Ausgaben des neuronalen Netzwerks in eine Textdarstellung. <\/p>\n

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

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

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