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

Recognition of Handwritten Chinese Text by Segmentation: A  Segment-annotation-free Approach

Deep learning based methods have been dominating the text recognition tasks
in different and multilingual scenarios. The offline handwritten Chinese text
recognition (HCTR) is one of the most challenging tasks because it involves
thousands of characters, variant writing styles and complex data collection
process. Recently, the recurrent-free architectures for text recognition
appears to be competitive as its highly parallelism and comparable results. In
this paper, we build the models using only the convolutional neural networks
and use CTC as the loss function. To reduce the overfitting, we apply dropout
after each max-pooling layer and with extreme high rate on the last one before
the linear layer. The CASIA-HWDB database is selected to tune and evaluate the
proposed models. With the existing text samples as templates, we randomly
choose isolated character samples to synthesis more text samples for training.
We finally achieve 6.81% character error rate (CER) on the ICDAR 2013
competition set, which is the best published result without language model
correction.

本文研究基于深度学习的方法在离线手写中文文本识别领域的应用，使用只有卷积神经网络的模型和 CTC 损失函数实现，采用 Dropout 方法以防止过拟合，最终在 ICDAR 2013 竞赛数据集上实现了 6.81% 的字符错误率，为同等条件下最好的研究结果。

离线手写中文文本识别与卷积神经网络

Offline Handwritten Chinese Text Recognition with Convolutional Neural  Networks

This paper proposes an end-to-end framework, namely fully convolutional
recurrent network (FCRN) for handwritten Chinese text recognition (HCTR).
Unlike traditional methods that rely heavily on segmentation, our FCRN is
trained with online text data directly and learns to associate the pen-tip
trajectory with a sequence of characters. FCRN consists of four parts: a
path-signature layer to extract signature features from the input pen-tip
trajectory, a fully convolutional network to learn informative representation,
a sequence modeling layer to make per-frame predictions on the input sequence
and a transcription layer to translate the predictions into a label sequence.
The FCRN is end-to-end trainable in contrast to conventional methods whose
components are separately trained and tuned. We also present a refined beam
search method that efficiently integrates the language model to decode the FCRN
and significantly improve the recognition results.
We evaluate the performance of the proposed method on the test sets from the
databases CASIA-OLHWDB and ICDAR 2013 Chinese handwriting recognition
competition, and both achieve state-of-the-art performance with correct rates
of 96.40% and 95.00%, respectively.

本文提出了一种名为全卷积循环网络 (FCRN) 的端到端框架用于手写汉字文本识别。FCRN 基于在线文本数据进行训练，不像传统方法依赖于分割，它学习将笔尖轨迹与字符序列关联起来。本文还提出了一种优化的波束搜索方法，有效地集成语言模型来解码 FCRN 并显著提高识别结果。在 CASIA-OLHWDB 和 ICDAR 2013 数据集上进行测试，分别获得 96.40% 和 95.00% 的正确率。