Luận văn thạc sĩ Khoa học máy tính: Nhận diện các tạp chí hiện đại của Nhật Bản bằng cách kết hợp học sâu và mô hình ngôn ngữ

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As one of the most culturally rich countries in the world, Japan also has a rich history of magazines In modern Japanese magazines which were published during the centuries XIX - XX, the usage of Japanese is similar with the current style of the Japanese language However, most of those documents are not digitized, only stored as images Due to their importance to Japanese culture, history and other socio-scientific topics, the problem of using computers to help identify these image-based modern magazines have been investigated from research and widely dissemined through the use of different methods in Deep Learning (Deep Learning) and Computer Vision (Computer Vision) However, these methods and models are still limited to achieve strong performance in recognizing handwriting images, especially uncommon Kanji characters

The purpose of this research is to develop a deep learning-based language model and integrate it into the current OCR system for Japanese modern magazine documents To automatically extract texts from those images accurately is the goal of this research, of which I vision the contributions as follows

- I develop a language model based deep learning techniques for modern Japanese magazines to improve the accuracy of the current OCR;

- I propose a combination strategy between the current OCR and our language model The strategy will learn where the system should rely on OCR (eg Hiragana and Common kanji characters recognized correctly by OCR) or language model (uncommon Kanji character are frequently recognized incorrectly by OCR, the system should rely on the language model)

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