Handwritten text line detection based on vision transformer

Kaihe Zhong

doi:10.1117/12.3037942

7 August 2024 Handwritten text line detection based on vision transformer

Kaihe Zhong

Author Affiliations +

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322929 (2024) https://doi.org/10.1117/12.3037942
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

There are various challenges in text detection of handwritten document images, such as dense text, complex layout, and noise interference. In order to better detect handwritten text lines in document, this paper combines ViT with PAN++ network models, utilizes ViT's powerful image understanding ability, and combines pixel aggregation algorithms to propose the PAViT network. The improved network model can effectively address various challenges in document images, such as dense text, mixed print and handwriting, background noise, etc. It accurately detects handwritten text lines in document images and performs experiments on the SCUT-HCCDoc dataset, achieving good results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kaihe Zhong "Handwritten text line detection based on vision transformer", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322929 (7 August 2024); https://doi.org/10.1117/12.3037942

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KEYWORDS

Detection and tracking algorithms

Image segmentation

Transformers

Convolution

Feature extraction

Image processing

Education and training

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