Digit recognition in digital measuring instruments is essential for automating calibration and measurement tasks, particularly to instruments without serial interface feature for data acquisition. This paper presents systematically reviews of recent techniques used (2017-2025) in digit recognition implemented in digital measuring instruments. The literature search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and identified 43 relevant studies. The finding underscores the gradually shifted techniques from traditional Optical Character Recognition (OCR) and machine learning classifier with feature engineering, towards deep learning-based approaches. YOLO-based object detection has emerged as the dominant trend with real-time and high accuracy digit recognition. Future work should prioritize the development of public datasets of digital measuring instruments for benchmarking various techniques proposed. Research opportunities of digit recognition are still very wide open, especially for industrial digital measuring instruments, considering the various digital instrument types and diverse of the measurement fields. © 2025 IEEE.