  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.
