基于跨模态深度度量学习的甲骨文字识别

Oracle Character Recognition Based on Cross-Modal Deep Metric Learning

甲骨文字图像可以分为拓片甲骨文字与临摹甲骨文字两类.拓片甲骨文字图像是从龟甲、兽骨等载体上获取的原始拓片图像,临摹甲骨文字图像是经过专家手工书写得到的高清图像.拓片甲骨文字样本难以获得,而临摹文字样本相对容易获得.为了提高拓片甲骨文字识别的性能,本文提出一种基于跨模态深度度量学习的甲骨文字识别方法,通过对临摹甲骨文字和拓片甲骨文字进行共享特征空间建模和最近邻分类,实现了拓片甲骨文字的跨模态识别.实验结果表明,在拓片甲骨文字识别任务上,本文提出的跨模态学习方法比单模态方法有明显的提升,同时对新类别拓片甲骨文字也能增量识别.

There are two types of oracle character images:handprinted ones that are clean,and ones scanned from bones and shells that are noised.The collection of handprinted samples is easier than that of scanned images.Therefore,to improve the recognition of scanned oracle characters,we propose a method based on cross-modal deep metric learning to take advantage of the handprinted samples.Via shared feature space learning using cross-modal handprinted and scanned samples,scanned characters can be recognized by nearest neighbor classification in the shared space.Experimental results demonstrate that the proposed method not only achieves better performance in oracle character recognition but also can recognize new categories incrementally.