Portmanteauing Features for Scene Text Recognition

Page view(s)
24
Checked on Jun 15, 2024
Portmanteauing Features for Scene Text Recognition
Title:
Portmanteauing Features for Scene Text Recognition
Journal Title:
2022 26th International Conference on Pattern Recognition (ICPR)
Keywords:
Publication Date:
29 November 2022
Citation:
Tan, Y. L., Yu Kai Chew, E., Kong, A. W.-K., Kim, J.-J., & Lim, J. H. (2022). Portmanteauing Features for Scene Text Recognition. 2022 26th International Conference on Pattern Recognition (ICPR). https://doi.org/10.1109/icpr56361.2022.9956468
Abstract:
Scene text images have different shapes and are subjected to various distortions, e.g. perspective distortions. To handle these challenges, the state-of-the-art methods rely on a rectification network, which is connected to the text recognition network. They form a linear pipeline which uses text rectification on all input images, even for images that can be recognized without it. Undoubtedly, the rectification network improves the overall text recognition performance. However, in some cases, the rectification network generates unnecessary distortions on images, resulting in incorrect predictions in images that would have otherwise been correct without it. In order to alleviate the unnecessary distortions, the portmanteauing of features is proposed. The portmanteau feature, inspired by the portmanteau word, is a feature containing information from both the original text image and the rectified image. To generate the portmanteau feature, a non-linear input pipeline with a block matrix initialization is presented. In this work, the transformer is chosen as the recognition network due to its utilization of attention and inherent parallelism, which can effectively handle the portmanteau feature. The proposed method is examined on 6 benchmarks and compared with 13 state-of-the-art methods. The experimental results show that the proposed method outperforms the state-of-the-art methods on various of the benchmarks.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Nanyang Technological University - NTU Internal Funding – Accelerating Creativity and Excellence
Grant Reference no. : NTU–ACE2020-03
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2831-7475
Files uploaded:

File Size Format Action
icpr2022-final.pdf 990.86 KB PDF Request a copy