论文标题
俄罗斯手写文字的基于注意的完全封闭的CNN-BGRU
Attention-based Fully Gated CNN-BGRU for Russian Handwritten Text
论文作者
论文摘要
这项研究通过接受哈萨克语和俄罗斯语言培训的注意编号编码网络处理手写文本的任务。我们开发了一种基于完全门控的CNN的新型深神经网络模型，由多个双向GRU和注意力机制支持，以操纵实现0.045个字符错误率（CER），0.192个单词错误率（WER）和0.253序列误差率（SER）的复杂特征，对于第一个测试数据集和0.064 CER和0.24 WER和0.24 WER和0.24 WERET和0.24 WERET。另外，我们通过利用TAHN和输入功能的多个输出功能的优势来提出完全封闭式的层，这项提议的工作取得了更好的结果，我们在手写的哈萨克和俄罗斯数据库（HKR）上尝试了模型。我们的研究是HKR数据集上的第一批工作，并向其他大多数现有模型展示了最先进的结果。
This research approaches the task of handwritten text with attention encoder-decoder networks that are trained on Kazakh and Russian language. We developed a novel deep neural network model based on Fully Gated CNN, supported by Multiple bidirectional GRU and Attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER) and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Also, we propose fully gated layers by taking the advantage of multiple the output feature from Tahn and input feature, this proposed work achieves better results and We experimented with our model on the Handwritten Kazakh & Russian Database (HKR). Our research is the first work on the HKR dataset and demonstrates state-of-the-art results to most of the other existing models.
