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Efficient Scene Text Decision Algorithm for Vehicle Text Recognition
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PRE
AI
DOI：10.3745/JIPS.04.0364.png)
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Abstract
En 中文
Simultaneous localization and mapping (SLAM) is a core technology in robotics and autonomous navigation. Visual SLAM has gained attention through advances in object recognition enabling landmark-based mapping. However, a critical gap exists in handling inconsistencies arising from repeated text recognition processes, a challenge that existing studies have largely overlooked. To address this, we propose an innovative algorithm that enhances text recognition accuracy for mobile platforms. Our method excels in tracking objects and consistently recognizing and determining textual content in recurrent scenes. Through rigorous experimentation, we demonstrate that our algorithm significantly improves real-time text recognition accuracy by mitigating errors inherent in conventional approaches. This advancement not only refines the reliability of text-based Visual SLAM but also broadens its applicability in dynamic, text-rich environments. Our work paves the way for more robust and efficient autonomous navigation systems, particularly in urban landscapes where textual cues are abundant.
Keywords:
Decision Fusion
Optical Character Recognition (OCR)
Scene Text Detection (STD)
Text SLAM
Text
Tracking
Journal
J
IF:
0.5
Papers:
33
Citations:
0