Due to the diversity of climate and environment in China, the frequent occurrence of extreme rainfall events has brought great challenges to flood prevention. Water level measurement is one of the important research topics of flood prevention. Recently, the image-based water level recognition method has become an important part of water level measurement research due to its advantages in easy installation, low cost, and zero need of manual reading. However, there are two mainly shortcomings of the existing image-based water level recognition methods: (1) severely affected by light intensity and (2) low accuracy of water level recognition for stained water gauges. To solve these two problems, this paper proposes a water level recognition method in consideration of complex scenarios. This method first uses a semantic segmentation convolutional neural network to extract the water gauge mask, and then uses the YOLOv5 object detection network to extract the letter “E” on the water gauge. Based on the character sequence inspection strategy, the algorithm dynamically compensates for the missed detection of characters of stained water gauges. Through a large number of experiments, the proposed water level measurement method has good robustness in complex scenarios, meeting the needs of flash flood defense.