用GAN生成手写字符数据，提升复杂连笔字识别准确率
High Cursive Complex Character Recognition using GAN External Classifier
- 构建外部判别器与GAN结合，生成带噪声的伪造手写图像用于数据增强
- 在复杂连笔字符上，ADA-GAN模型比传统CNN准确率更高且更稳定
- 适合需要处理高难度手写文字识别的场景，如古籍数字化、银行票据录入
手写字符因其复杂且连笔的特性，比简单非连笔字符更难分类。本文提出一种结合生成对抗网络的外部分类器，用于识别高度连笔和复杂的汉字。生成器网络生成虚假的手写字符图像，经添加对抗性扰动噪声，并通过判别器获得置信度超过阈值后，用于训练数据增强。实验表明，随着字符复杂度增加，卷积神经网络的准确率下降，而所提出的ADA-GAN模型在连笔和复杂字符上仍保持更强的鲁棒性和有效性。
原文摘要 · Abstract (English)
Handwritten characters can be trickier to classify due to their complex and cursive nature compared to simple and non-cursive characters. We present an external classifier along with a Generative Adversarial Network that can classify highly cursive and complex characters. The generator network produces fake handwritten character images, which are then used to augment the training data after adding adversarially perturbed noise and achieving a confidence score above a threshold with the discriminator network. The results show that the accuracy of convolutional neural networks decreases as character complexity increases, but our proposed model, ADA-GAN, remains more robust and effective for both cursive and complex characters.
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