手写体文本识别技术可以将手写文档转录成可编辑的数字文档。但由于手写的书写风格迥异、文档结构千变万化和字符分割识别精度不高等问题,基于神经网络的手写体英文文本识别仍面临着许多挑战。针对上述问题,提出基于卷积神经网络(CNN)和...手写体文本识别技术可以将手写文档转录成可编辑的数字文档。但由于手写的书写风格迥异、文档结构千变万化和字符分割识别精度不高等问题,基于神经网络的手写体英文文本识别仍面临着许多挑战。针对上述问题,提出基于卷积神经网络(CNN)和Transformer的手写体英文文本识别模型。首先利用CNN从输入图像中提取特征,而后将特征输入到Transformer编码器中得到特征序列每一帧的预测,最后经过链接时序分类(CTC)解码器获得最终的预测结果。在公开的IAM(Institut für Angewandte Mathematik)手写体英文单词数据集上进行了大量的实验结果表明,该模型获得了3.60%的字符错误率(CER)和12.70%的单词错误率(WER),验证了所提模型的可行性。展开更多
In this paper,we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.We first discuss models such as recurrent neural networks(RNNs) a...In this paper,we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.We first discuss models such as recurrent neural networks(RNNs) and convolutional neural networks(CNNs) that can effectively exploit variablelength contextual information,and their various combination with other models.We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system,the connectionist temporal classification(CTC) criterion,and the attention-based sequenceto-sequence translation model.We further illustrate robustness issues in speech recognition systems,and discuss acoustic model adaptation,speech enhancement and separation,and robust training strategies.We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.展开更多
文摘手写体文本识别技术可以将手写文档转录成可编辑的数字文档。但由于手写的书写风格迥异、文档结构千变万化和字符分割识别精度不高等问题,基于神经网络的手写体英文文本识别仍面临着许多挑战。针对上述问题,提出基于卷积神经网络(CNN)和Transformer的手写体英文文本识别模型。首先利用CNN从输入图像中提取特征,而后将特征输入到Transformer编码器中得到特征序列每一帧的预测,最后经过链接时序分类(CTC)解码器获得最终的预测结果。在公开的IAM(Institut für Angewandte Mathematik)手写体英文单词数据集上进行了大量的实验结果表明,该模型获得了3.60%的字符错误率(CER)和12.70%的单词错误率(WER),验证了所提模型的可行性。
文摘In this paper,we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.We first discuss models such as recurrent neural networks(RNNs) and convolutional neural networks(CNNs) that can effectively exploit variablelength contextual information,and their various combination with other models.We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system,the connectionist temporal classification(CTC) criterion,and the attention-based sequenceto-sequence translation model.We further illustrate robustness issues in speech recognition systems,and discuss acoustic model adaptation,speech enhancement and separation,and robust training strategies.We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.
