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Digital management of the contact network pillars is a critical component of operating and maintaining electrified railways. Creating a digital ledger of catenary pillars based on mobile video is an efficient, cost-effective, and convenient technological method. To achieve accurate recognition of the plate numbers on catenary pillars in mobile video image sequence, we propose a catenary pillar recognition algorithm based on region correlation and an improved SVTR network. Firstly, to address the challenges posed by overlapping contact network pillar areas and complex structural patterns in video images, we use the YOLO v4 network to separately detect catenary pillar areas and number plate areas in single-frame images. The nearest catenary pillar position and corresponding number plate area are then determined by calculating overlapping areas. Furthermore, we use an improved SVTR-tiny network for catenary pillar number text recognition to address the issues of diverse pillar number plate scales and variable character length, and transfer learning methods are introduced to enhance the model’s recognition accuracy for complex plate numbers and its generalization performance to different line scenes. Through testing on a mobile video dataset collected from actual high-speed railway lines, the test results show that the algorithm achieves a positioning recall rate of 98.01% for the nearest pillar number in the field of view and a pillar number text recognition accuracy of 96.13%, and the method is suitable for the catenary pillar structure supporting the construction of major high-speed railway trunk lines in China.