基于深度学习的拓印文字识别实例研究

2020 International SoC Design Conference (ISOCC) Pub Date : 2020-10-21 DOI:10.1109/ISOCC50952.2020.9333064

Zelin Meng, Zhiyu Zhang, Lin Meng, Hiroyuki Tomiyama

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引用次数: 0

摘要

本文以实例研究了利用深度神经网络对拓印文字的分类性能。在评价实验中，采用了几种主流的深度神经网络来实现拓印文字的识别。这项工作的目的是为了检验流行的神经网络的分类性能，并为我们未来的工作收集必要的信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Case Study on Rubbing Character Recognition Based on Deep Learning

This paper presents an case study which explored the classification performance of rubbing characters by utilizing deep neural networks. In the evaluation experiments, several mainstream deep neural networks are employed to realize the recognition of the rubbing characters. The purpose of this work is to examine the classification performance of the prevalent neural networks and collect the necessary information for our future works.

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2020 International SoC Design Conference (ISOCC)

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