Text Detection and Recognition for Robot Localization

Raisi, Z.; Zelek, J.

doi:10.22061/jecei.2023.9857.658

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	Text Detection and Recognition for Robot Localization
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 16 مهر 1402 اصل مقاله (1.26 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9857.658
نویسندگان
Z. Raisi^* ¹؛ J. Zelek²
¹University of Waterloo, Waterloo, Canada and Chabahar Maritime University, Chabahar, Iran.
²Systems Design Engineering Department, University of Waterloo, Canada.
تاریخ دریافت: 05 تیر 1402، تاریخ بازنگری: 14 شهریور 1402، تاریخ پذیرش: 17 شهریور 1402
چکیده
Background and Objectives: Signage is everywhere, and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due to pose, irregular text instances, illumination variations, viewpoint changes, and occlusion factors. Methods: This paper proposes an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. The proposed model leverages a pre-trained Vision Transformer based (ViT) architecture combined with a multi-task transformer-based text detector more suitable for the VPR task. Our central contribution is introducing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. We first equip the ViT backbone using a masked autoencoder (MAE) to capture partially occluded characters to address the occlusion problem. Then, we use a multi-task prediction head for the proposed model to handle arbitrary shapes of text instances with polygon bounding boxes. Results: The evaluation of the proposed architecture's performance for VPR involved conducting several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The well-known evaluation metric, Precision-Recall, was employed to measure the performance of the proposed pipeline. The final model achieved the following performances, Recall = 0.93 and Precision = 0.8, upon testing on this benchmark. Conclusion: The initial experimental results show that the proposed model outperforms the state-of-the-art (SOTA) methods in comparison to the SCTP dataset, which confirms the robustness of the proposed end-to-end scene text detection and recognition model.
کلیدواژه‌ها
Text detection؛ Text Recognition؛ Robotics Localization؛ Deep Learning؛ Visual Place Recognition

مراجع
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Text Detection and Recognition for Robot Localization