Recently, the hybrid convolutional neural network hidden Markov model
(CNN-HMM) has been introduced for offline handwritten Chinese text recognition
(HCTR) and has achieved state-of-the-art performance. However, modeling each of
the large vocabulary of Chinese characters with a uniform and fixed number of
hidden states requires high memory and computational costs and makes the tens
of thousands of HMM state classes confusing. Another key issue of CNN-HMM for
HCTR is the diversified writing style, which leads to model strain and a
significant performance decline for specific writers. To address these issues,
we propose a writer-aware CNN based on parsimonious HMM (WCNN-PHMM). First,
PHMM is designed using a data-driven state-tying algorithm to greatly reduce
the total number of HMM states, which not only yields a compact CNN by state
sharing of the same or similar radicals among different Chinese characters but
also improves the recognition accuracy due to the more accurate modeling of
tied states and the lower confusion among them. Second, WCNN integrates each
convolutional layer with one adaptive layer fed by a writer-dependent vector,
namely, the writer code, to extract the irrelevant variability in writer
information to improve recognition performance. The parameters of
writer-adaptive layers are jointly optimized with other network parameters in
the training stage, while a multiple-pass decoding strategy is adopted to learn
the writer code and generate recognition results. Validated on the ICDAR 2013
competition of CASIA-HWDB database, the more compact WCNN-PHMM of a 7360-class
vocabulary can achieve a relative character error rate (CER) reduction of 16.6%
over the conventional CNN-HMM without considering language modeling. By
adopting a powerful hybrid language model (N-gram language model and recurrent
neural network language model), the CER of WCNN-PHMM is reduced to 3.17%.

本文介绍了一种基于数据驱动状态捆绑算法的作家感知 CNN-PHMM 方法，用于离线手写中文文本识别。这个方法使用卷积神经网络和廉价的 HMM 模型，通过集成一个适应层来处理文本中作家的不同书写风格，并分离同音字，最终比之前的方法提高了 16.6％的字符错误率 (CER)。在结合 N-gram 语言模型和递归神经网络语言模型后，CER 进一步降低至 3.17％。