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• W2891282199 abstract "Most dense word embedding methods are based on statistics and semantic information currently. However, for hieroglyphs, these methods ignore the visual information underlaid in the characters, moreover this visual information in the expression of characters plays an extremely important role. Therefore, the visual information can be uncovered from the single character image through Convolutional Neural Network (CNN). Compared with the mainstream methods, the CNN method is inferior in efficiency and precision. In this study, we present a novel model called Img2Vec: using Principal Component Analysis (PCA) to generate word embedding vectors. Because the semantic and the visual information of the characters are complementary, we feed Word2Vec and Img2Vec embeddings into two different fusion models to implement text classification. Experiments show that our Img2Vec model has significant improvements in training time and precision. Finally, the visualizations of our Img2Vec character embedding prove that our model has a state-of-the-art representation of the visual information." @default.
• W2891282199 created "2018-09-27" @default.
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• W2891282199 date "2018-01-01" @default.
• W2891282199 modified "2023-09-27" @default.
• W2891282199 title "Visual-Based Character Embedding via Principal Component Analysis" @default.
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• W2891282199 doi "https://doi.org/10.1007/978-981-13-2203-7_16" @default.
• W2891282199 hasPublicationYear "2018" @default.
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