Scene text recognition(STR)is the task of recognizing character sequences in natural scenes.Although STR method has been greatly developed,the existing methods still can't recognize any shape of text,such as very ...Scene text recognition(STR)is the task of recognizing character sequences in natural scenes.Although STR method has been greatly developed,the existing methods still can't recognize any shape of text,such as very rich curve text or rotating text in daily life,irregular scene text has complex layout in two-dimensional space,which is used to recognize scene text in the past Recently,some recognizers correct irregular text to regular text image with approximate 1D layout,or convert 2D image feature mapping to one-dimensional feature sequence.Although these methods have achieved good performance,their robustness and accuracy are limited due to the loss of spatial information in the process of two-dimensional to one-dimensional transformation.In this paper,we proposes a framework to directly convert the irregular text of two-dimensional layout into character sequence by using the relationship attention module to capture the correlation of feature mapping Through a large number of experiments on multiple common benchmarks,our method can effectively identify regular and irregular scene text,and is superior to the previous methods in accuracy.展开更多
A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can ...A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.展开更多
文摘Scene text recognition(STR)is the task of recognizing character sequences in natural scenes.Although STR method has been greatly developed,the existing methods still can't recognize any shape of text,such as very rich curve text or rotating text in daily life,irregular scene text has complex layout in two-dimensional space,which is used to recognize scene text in the past Recently,some recognizers correct irregular text to regular text image with approximate 1D layout,or convert 2D image feature mapping to one-dimensional feature sequence.Although these methods have achieved good performance,their robustness and accuracy are limited due to the loss of spatial information in the process of two-dimensional to one-dimensional transformation.In this paper,we proposes a framework to directly convert the irregular text of two-dimensional layout into character sequence by using the relationship attention module to capture the correlation of feature mapping Through a large number of experiments on multiple common benchmarks,our method can effectively identify regular and irregular scene text,and is superior to the previous methods in accuracy.
文摘A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.
