Affiliations:
Information Technology and Cultural Management Institute, Hebei Institute of Communications, Shijiazhuang, Hebei, China
Correspondence:
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Corresponding author: Yunze Wang, Information Technology and Cultural Management Institute, Hebei Institute of Communications, Shijiazhuang, Hebei, 051430, China. E-mail: [email protected].
Abstract: This paper proposes an intelligent warehouse-picking approach using radio frequency identification (RFID) indoor positioning and natural language processing (NLP) speech recognition. A forward maximum matching algorithm segments speech into domain terminology. Location was estimated by RFID signal strengths between reference tags and pickers. Simulation results demonstrated a 50% reduction in segmentation runtime versus conventional methods. Speech recognition accuracy reached 90–95%, improving by 23% over baseline. Positioning accuracy also increased substantially. The techniques can reduce picking errors and costs. Further work should evaluate performance in real-world environments.