Natural Scene Text Recognition based on Multi-Head Self Attention and Long Short-Term Memory Network
With the continuous development of computer vision and natural language processing technologies,natural scene text detection and recognition has become one of the research hotspots in the field of computer vision.A natural scene text detection and recognition method based on multi-head attention mechanism and long short-term memory(LSTM)network is proposed.The method combines object detection algorithms and sequence recognition algorithms to precisely locate and extract features of text regions in images by using a multi-head attention mechanism.Then,the extracted features are encoded and decoded by using LSTM network to achieve accurate rec-ognition of text in natural scenes.In the text detection stage,a deep learning-based object detection algorithm is used,combined with a multi-head attention mechanism,to capture text information of different scales and orientations in the image by parallel computing multi-ple independent attention heads,thereby improving the accuracy and robustness of text detection.In the text recognition stage,LSTM network is used to model the detected text regions and converts text information in the image into readable character sequences through the encoding and decoding process.Experimental results show that the method proposed achieves excellent performance in natural scene text detection and recognition tasks.Compared with existing methods,the proposed method has improved accuracy and robustness,espe-cially in handling complex backgrounds and diverse text.
text detection and recognitionmulti-head attention mechanismnatural scene textLSTM
万方数据
维普
