A single-shot text detector base scale wise feature aggregation module

Kunpeng Wang

doi:10.1117/12.3038123

7 August 2024 A single-shot text detector base scale wise feature aggregation module

Kunpeng Wang

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132290O (2024) https://doi.org/10.1117/12.3038123
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Handwritten ancient documents present unique challenges due to paper aging, ink fading, and blurred handwriting, making their text detection more difficult than standard tasks. Simultaneously, the layout structure is notably intricate, featuring double columns interspersed with single columns along with a blend of images and text, presenting challenges for detection. Therefore, considering the challenges of images from ancient documents, a single stage text detection method on Scale wise Feature Aggregation Module (SFAM) is proposed. It builds on fully convolutional networks to directly generate character level predictions, that identifying redundant and slow intermediate steps Furthermore, by fusing feature maps of different scales to encode information from different receptive field sizes and introducing channel attention mechanisms to allow issues caused by scale variations among different object instances, effective and accurate detection of characters in ancient documents is achieved. In order to assess the effective of the approach, we carried out experiments utilizing the MTHv2 dataset. Our findings indicate that the proposed method surpasses the majority of other text detectors in terms of precision, recall, and F1 score.

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

Citation Download Citation

Kunpeng Wang "A single-shot text detector base scale wise feature aggregation module", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132290O (7 August 2024); https://doi.org/10.1117/12.3038123

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KEYWORDS

Object detection

Education and training

Sensors

Feature extraction

Performance modeling

Data modeling

Image segmentation

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