The past decade has seen the rapid development of text detection based on deep learning.However,current methods of Chinese character detection and recognition have proven to be poor.The accuracy of segmenting text box...The past decade has seen the rapid development of text detection based on deep learning.However,current methods of Chinese character detection and recognition have proven to be poor.The accuracy of segmenting text boxes in natural scenes is not impressive.The reasons for this strait can be summarized into two points:the complexity of natural scenes and numerous types of Chinese characters.In response to these problems,we proposed a lightweight neural network architecture named CTSF.It consists of two modules,one is a text detection network that combines CTPN and the image feature extraction modules of PVANet,named CDSE.The other is a literacy network based on spatial pyramid pool and fusion of Chinese character skeleton features named SPPCNN-SF,so as to realize the text detection and recognition,respectively.Our model performs much better than the original model on ICDAR2011 and ICDAR2013(achieved 85%and 88%F-measures)and enhanced the processing speed in training phase.In addition,our method achieves extremely performance on three Chinese datasets,with accuracy of 95.12%,95.56%and 96.01%.展开更多
This paper proposes a new approach to the water flow algorithm for text line segmentation. In the basic method the hypothetical water flows under few specified angles which have been defined by water flow angle as par...This paper proposes a new approach to the water flow algorithm for text line segmentation. In the basic method the hypothetical water flows under few specified angles which have been defined by water flow angle as parameter. It is applied to the document image frame from left to right and vice versa. As a result, the unwetted and wetted areas are established. These areas separate text from non-text elements in each text line, respectively. Hence, they represent the control areas that are of major importance for text line segmentation. Primarily, an extended approach means extraction of the connected-components by bounding boxes over text. By this way, each connected component is mutually separated. Hence, the water flow angle, which defines the unwetted areas, is determined adaptively. By choosing appropriate water flow angle, the unwetted areas are lengthening which leads to the better text line segmentation. Results of this approach are encouraging due to the text line segmentation improvement which is the most challenging step in document image processing.展开更多
A new stick text segmentation method based on the sub connected area analysis is introduced in this paper. The foundation of this method is the sub connected area representation of text image that can represent all c...A new stick text segmentation method based on the sub connected area analysis is introduced in this paper. The foundation of this method is the sub connected area representation of text image that can represent all connected areas in an image efficiently. This method consists mainly of four steps: sub connected area classification, finding initial boundary following point, finding optimal segmentation point by boundary tracing, and text segmentation. This method is similar to boundary analysis method but is more efficient than boundary analysis.展开更多
基金This work is supported by the National Natural Science Foundation of China(61872231,61701297).
文摘The past decade has seen the rapid development of text detection based on deep learning.However,current methods of Chinese character detection and recognition have proven to be poor.The accuracy of segmenting text boxes in natural scenes is not impressive.The reasons for this strait can be summarized into two points:the complexity of natural scenes and numerous types of Chinese characters.In response to these problems,we proposed a lightweight neural network architecture named CTSF.It consists of two modules,one is a text detection network that combines CTPN and the image feature extraction modules of PVANet,named CDSE.The other is a literacy network based on spatial pyramid pool and fusion of Chinese character skeleton features named SPPCNN-SF,so as to realize the text detection and recognition,respectively.Our model performs much better than the original model on ICDAR2011 and ICDAR2013(achieved 85%and 88%F-measures)and enhanced the processing speed in training phase.In addition,our method achieves extremely performance on three Chinese datasets,with accuracy of 95.12%,95.56%and 96.01%.
文摘This paper proposes a new approach to the water flow algorithm for text line segmentation. In the basic method the hypothetical water flows under few specified angles which have been defined by water flow angle as parameter. It is applied to the document image frame from left to right and vice versa. As a result, the unwetted and wetted areas are established. These areas separate text from non-text elements in each text line, respectively. Hence, they represent the control areas that are of major importance for text line segmentation. Primarily, an extended approach means extraction of the connected-components by bounding boxes over text. By this way, each connected component is mutually separated. Hence, the water flow angle, which defines the unwetted areas, is determined adaptively. By choosing appropriate water flow angle, the unwetted areas are lengthening which leads to the better text line segmentation. Results of this approach are encouraging due to the text line segmentation improvement which is the most challenging step in document image processing.
基金Supported by the National "863" Hi-Tech Program of China.
文摘A new stick text segmentation method based on the sub connected area analysis is introduced in this paper. The foundation of this method is the sub connected area representation of text image that can represent all connected areas in an image efficiently. This method consists mainly of four steps: sub connected area classification, finding initial boundary following point, finding optimal segmentation point by boundary tracing, and text segmentation. This method is similar to boundary analysis method but is more efficient than boundary analysis.
