Frequent and more accurate water level measurement will allow for a more efficient distribution of water, resulting in less water loss. Therefore in this paper we propose a novel method for accurate water level detection and measurement applied on images of staff gauges, retrieved from mobile device camera. In the first step, we propose fast segmentation of the staff gauge using a 2-class random forest classifier based on a feature vector of textons. To obtain bars and numbers we apply Gaussian Mixture Model segmentation followed by optical character recognition based on random forest classifier and bar detection using shape moments. Based on the recognized lines and numbers a quadratic function for the water level measurement to obtain metric values is introduced. Finally, we propose a novel step for the water level line detection. The water level function and the detected water line provide the value of the water level based on the units on the staff-gauge. The water level can then be uploaded to a central server to determine if water flow needs to increase or decrease. Testing with a real world images from Dutch canals show very accurate detection with many different staff-gauge locations despite complex challenges of viewpoints variations, low quality images as well as changing illumination conditions.