Research on automatic segmentation and recognition of oracle bone script images based on deep learning technology

Oracle bone script is an important form of ancient Chinese writing, and its study is significant for understanding ancient society, culture, and the development of language. With technological advancements, particularly in computer vision and deep learning, the digital study of oracle bone script has become feasible. This research aims to enhance the efficiency of automatic segmentation and recognition of oracle bone script images through deep learning technology. First, this paper investigates the unique challenges presented by oracle bone script images, such as dotted noise, artificial textures, and intrinsic patterns, and applies image preprocessing techniques such as gray-scaling, binarization, and noise reduction to optimize image quality. Second, convolutional neural networks (CNNs) are employed for image segmentation to accurately isolate individual characters within the oracle bone script images. Then, recurrent neural networks (RNNs) are used to automatically recognize the segmented characters, achieving the conversion from images to text. Finally, tests on original rubbings validate the model's high accuracy and reliability in automatic single-character segmentation and recognition. This study not only improves the level of automation in the image processing of oracle bone script but also provides methods and references for the image processing of other ancient artifacts.