We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond best-reported results on handwriting text recognition (HTR) tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in HTR tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network used, they could also be applied to enhance the performance of other networks and approaches to HTR. Extensive experiments on ten handwritten text datasets show that HandWritten Blots augmentation and StackMix significantly improve the quality of HTR models
翻译:我们引入了两种数据增强技术,它们与Resnet-BILSTM-CT网络一起使用,大大降低了字错误率(WER)和字符错误率(CER),超出了笔迹文本识别(HTR)任务的最佳报告结果。我们采用了一种新型增强技术,模拟透透文字(HandWritten布洛茨)和基于印刷文本的手写文本生成方法(StackMix),这在HTR任务中证明非常有效。StackMix使用受微弱监督的框架来获取字符边界。由于这些数据增强技术独立于所使用的网络,它们还可以用于提高其他网络的性能和HTR方法。十种手写文本数据集的广泛实验表明HandWrittblots增强和StackMix显著改进了HTR模型的质量。