Oracle Bone Inscription Recognition Based on Isometric Mapping Algorithm

In this paper, the Isometric Mapping (ISOMAP) algorithm is applied to recognize oracle bone inscription images. First, the sample set undergoes denoising and size normalization as preprocessing steps. Subsequently, a gray-value matrix is extracted from the images as their feature representation. The ISOMAP algorithm is then implemented to obtain a low-dimensional embedding of the sample set. Following this, the classification is performed by selecting the label corresponding to the nearest neighbor category with the highest frequency around the test sample. By optimizing the parameters of the ϵ -neighborhood and N , the recognition accuracy reaches 93.3%. Finally, the performance of ISOMAP is compared with other manifold learning algorithms. Experimental results demonstrate that ISOMAP achieves a higher average recognition rate and lower computational time compared to its counterparts. Therefore, ISOMAP algorithm proves to be an effective tool for oracle bone inscription recognition.