关键词: 古籍汉字检索, 卷积神经网络, NetVLAD, 视觉特征, 特征编码

Abstract: The retrieval of ancient characters is a part of the digitization work of ancient books. Ancient Chinese books often exhibit inconsistent printing glyphs and a wide variety of font types. Using visual features for Chinese character retrieval is an effective solution. A Chinese Character Feature Extraction and Encoding Network (CFEENet) was proposed. Firstly, a convolutional neural network was used to extract the visual features of Chinese character images. Secondly, a trainable generalized vector aggregation layer, namely NetVLAD, was employed to aggregate and encode these visual features. Finally, cosine similarity was used to calculate the similarity of encodings for ancient Chinese character retrieval from ancient books. A visual analysis of the CFEENet encoding is carried out using t-distributed stochastic neighbor embedding, and it is found that the clusters formed by the CFEENet encoding have high density, small overlap between clusters, and high encoding resolution. The CFEENet method is evaluated across multiple ancient book datasets. The experimental result shows that CFEENet outperforms comparative methods such as Ancient Chinese Character Image Network (ACCINet) in terms of mean Average Precision (mAP) and F1 score in most scenarios, while demonstrating a good balance between retrieval quality and efficiency. The applicability and effectiveness of the CFEENet method is proved to solve the task of retrieving Chinese character in ancient book images.

Key words: ancient Chinese character retrieval, convolutional neural network, NetVLAD, visual feature, feature encoding