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Precision livestock farming depends critically on the ability to identify individual animals. One practical route for marking individual livestock is freeze branding with liquid nitrogen. With the march of technological progress, deep learning pipelines have begun to be deployed for reading such individual markings. The present work introduces a deep-learning-based pipeline for the localization and interpretation of oriented horse brands. In the first stage, Rotational YOLOv5 (R-YOLOv5) was harnessed to pinpoint the oriented brand, after which YOLOv5 processed the extracted brand patches to read the digits. Within that first stage, R-YOLOv5 departed from standard detection frameworks by embedding orientation directly into the YOLO architecture through Circle Smooth Label (CSL). In addition, Coordinate Attention (CA) was woven into the network to sharpen its focus on spatial positioning cues. These design choices boosted the fidelity with which oriented brands were detected. The second stage reframed digit reading as an object-detection problem, given the need for precise identification. The full brand number was then assembled by ordering the detection boxes according to their spatial coordinates. Experimental comparisons revealed that R-YOLOv5 outpaced competing rotating target detection methods, achieving an AP (Average Accuracy) of 95.6%, FLOPs of 17.4 G, and an inference rate of 14.3 fps. The digit reading stage delivered an mAP (mean Average Precision) of 95.77%, a weight footprint of 13.71 MB, and an inference rate of 68.6 fps. Through this two-stage design, brand numbers can be read reliably even against cluttered backgrounds, offering a robust yet lightweight solution for individual livestock identification.