Online and offline handwritten Chinese text recognition (HTCR) has been studied for decades. Early methods adopted oversegmentation-based strategies but suffered from low speed, insufficient accuracy, and high cost of character segmentation annotations. Recently, segmentation-free methods based on connectionist temporal classification (CTC) and attention mechanism, have dominated the field of HCTR. However, people actually read text character by character, especially for ideograms such as Chinese. This raises the question: are segmentation-free strategies really the best solution to HCTR? To explore this issue, we propose a new segmentation-based method for recognizing handwritten Chinese text that is implemented using a simple yet efficient fully convolutional network. A novel weakly supervised learning method is proposed to enable the network to be trained using only transcript annotations; thus, the expensive character segmentation annotations required by previous segmentation-based methods can be avoided. Owing to the lack of context modeling in fully convolutional networks, we propose a contextual regularization method to integrate contextual information into the network during the training stage, which can further improve the recognition performance. Extensive experiments conducted on four widely used benchmarks, namely CASIA-HWDB, CASIA-OLHWDB, ICDAR2013, and SCUT-HCCDoc, show that our method significantly surpasses existing methods on both online and offline HCTR, and exhibits a considerably higher inference speed than CTC/attention-based approaches.

提出一种基于分割的方法以识别手写中文文本，使用简单而有效的完全卷积网络实现。使用一种新颖的弱监督学习方法，只需使用交录注释即可训练网络；无需先前分割方法所需的昂贵字符分割标注。因为完全卷积网络缺乏上下文建模，所以提出了一种上下文规则化方法将上下文信息纳入训练，进一步提高了识别性能。在四个广泛使用的基准测试上进行了广泛实验，即CASIA-HWDB，CASIA-OLHWDB，ICDAR2013和SCUT-HCCDoc，结果表明我们的方法在在线和离线HCTR上都显著优于现有方法，并且比CTC / 基于注意力的方法具有更高的推断速度。

手写汉字文本分割识别：一种无需分段注释的方法