Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 1146-1154.doi: 10.13229/j.cnki.jdxbgxb.20210829

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Segmentation-based detector for traditional Chinese newspaper

Yu JIANG1,2(),Jia-zheng PAN1,He-huai CHEN1,Ling-zhi FU1,Hong QI1,2()   

  1. 1.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education，Jilin University，Changchun 130012，China
  • Received:2021-08-27 Online:2023-04-01 Published:2023-04-20
  • Contact: Hong QI E-mail:jiangyu2011@jlu.edu.cn;qihong@jlu.edu.cn

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Abstract:

Most of the research on text detection has been conducted on natural scene datasets， few on such specific scene. And the existing models are not good enough for text detection on traditional Chinese newspaper. In order to solve this problem， a segmentation-based text detector for traditional Chinese newspaper is proposed in this paper. The model uses Resnet50 and FPN as feature extraction network， employing a segmentation instance scaling and extension algorithm to generate the binary map for predicting text boxes. And the methods of surrounding filling and loop detection plus region coverage are proposed to enhance the detection effect. In addition， a traditional Chinese newspaper dataset is built to satisfy the research needs. The experimental results of this model on traditional Chinese newspaper dataset are around 0.9 and are improved by 5% to 7% compared with DBNet， which indicates that the model is effective and accurate for text detection on traditional Chinese newspaper.

Key words: computer application, specific scene, traditional Chinese newspaper, text embedded in images, text detection

CLC Number: 

  • TP391

Fig.1

Front architecture of model"

Fig.2

Image of two binarization formulas"

Fig.3

Average value of indicators under differentthresholds"

Fig.4

Example of test results"

Fig.5

Segmentation examples with different sizes"

Fig.6

Process of generating text boxes"

Fig.7

Image after covering part of text area"

Fig.8

Process of loop detection and region coverage"

Table 1

Performance with/without surrounding filling method"

模型RecallPrecisionF?score

不使用周围填补

使用周围填补

0.864

0.899

0.855

0.912

0.859

0.906

Table 2

Performance with/without segmentation instance scaling and extension algorithm"

模型RecallPrecisionF?score

不使用该方法

使用该方法

0.870

0.899

0.902

0.912

0.886

0.906

Table 3

Evaluation results on traditional newspapertext detection dataset"

模型RecallPrecisionF?score

MSER+SWT

CRAFT

FCENet

DBNet+MobileNetv3

DBNet+Resnet18

DBNet+Resnet50

本文

0.558

0.581

0.654

0.900

0.873

0.840

0.899

0.719

0.343

0.656

0.730

0.737

0.836

0.912

0.628

0.431

0.655

0.805

0.799

0.838

0.906

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