A Deep Learning-Based Oracle Feature Extraction and Recognition Method

In order to improve the recognition of Oracle bone inscriptions, we make relevant contributions to the translation of Oracle bone inscriptions. This paper proposes a deep learning-based Oracle feature extraction and recognition technology. The purpose of this paper is to identify the translation problem of Oracle bones by the related parameter model of science and engineering. The core component of the recognition in this paper is Oracle bones. It provides a single-radical combination structure of the Oracle bones character recognition method, which improves the recognition accuracy of Oracle bones. Select the stable value area. Furthermore, due to the scarcity of Oracle bone rubbings, this work provides a Fast RCNN model that can identify Oracle bone inscriptions holistically. In order to extract the high-level features of Oracle-bone combination words, the parameters and structure of the model are fine-tuned to realize the feature fusion of low-level representation and high-level representation on the Oracle-bone combination word set. The recognition accuracy of Oracle Character Recognition is very high. At the same time, this paper treats Oracle bones as a collection of radicals rather than individual characters, allowing the algorithm to identify new Oracle bones that have never been seen before, a technique known as zero-shot learning, which is crucial to Oracle bone research. Importance of application.