About Viola-Jones image classifier structure in the problem of stamp detection in document images

Matalov, Daniil P.; Usilin, Sergey A.; Arlazarov, Vladimir V.

doi:10.1117/12.2586842

Cited by 6 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use the modified Viola and Jones method to detect seals in a document image. The modifications include the simultaneous use of brightness features and Haar edge features and the processing of seal area features [14,18].…”

Section: Using Seal Imagesmentioning

confidence: 99%

Method for Analyzing the Structure of Noisy Images of Administrative Documents

Slavin,

Pliskin

2023

Studies in Systems, Decision and Control

View full text Add to dashboard Cite

The problem of extracting content elements (fields) from the images of administrative documents via descriptions of anchoring elements is considered. Administrative documents contain static elements and content elements (filled information). The static objects of the document model are the lines of the document structure and the words. Sets of objects united by properties and relationships are described. The text descriptor can contain attributes that distinguish it from similar descriptors. We suggest using combined descriptors of line segments and words. We showed experimentally that the extraction of object sets improves the recognition accuracy of the document fields by 17% and the accuracy of information extraction by 16%. For optical character recognition, we employed SDK Smart Document Engine in the experiment.

show abstract

Section: Using Seal Imagesmentioning

confidence: 99%

Method for Analyzing the Structure of Noisy Images of Administrative Documents

Slavin,

Pliskin

2023

Studies in Systems, Decision and Control

View full text Add to dashboard Cite

show abstract

“…Automatic drawing verification can improve the verification efficiency and save time. The Viola–Jones image classifier structure was used to identify seal images in document images ( Matalov et al, 2021 ). Rho et al (2020) identified the engineering drawings and extracted the row text in the document to automatically generate the building’s 3D model.…”

Section: Introductionmentioning

confidence: 99%

Improved Faster R-CNN for the Detection Method of Industrial Control Logic Graph Recognition

Wang

Yang

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

In the process of developing the industrial control SAMA logic diagram commonly used in the industrial process control system, there are some problems, that is, the size of logic diagram elements is small, the shape is various, similar element recognition is easily confused, and the detection accuracy is low. In this study, the faster R-CNN network has been improved. The original VGG16 network has been replaced by the ResNet101 network, and the residual value module was introduced to ensure the detailed features of the deep network. Then the industrial control logic diagram dataset was analyzed to improve the anchor size ratio through the K-means clustering algorithm. The candidate box screening problem was optimized by improving the non-maximum suppression algorithm. The elements were distinguished via the combination of the candidate box location and the inherent text, which improved the recognition accuracy of similar elements. An experimental platform was built using the TensorFlow framework based on the Windows system, and the improved method was compared with the original one by the control variable. The results showed that the performance of similar element recognition has been greatly enhanced through an improved faster R-CNN network.

show abstract