Toward Accurate Readings of Water Meters by Eliminating Transition Error: New Dataset and Effective Solution

Jiaxin Zhang, Daizhen Jia, Chongyu Liu, Dezhi Peng, Bangdong Chen, Xue Gao, Lianwen Jin

Abstract: Automatic reading of traditional mechanical water meters presents significant economic advantages. To achieve this objective, a cost-effective and efficient approach is to develop a computer vision-based recognition system that utilizes meter images as input and predicts the corresponding readings. However, this is challenging for pointer-type water meters, with significant difficulty in accurately determining transitions. Transition errors often occur when the pointer precisely indicates the transitional position, making it difficult to determine whether a transition should occur. In addition, unavoidable parallax introduced during the meter image acquisition process further complicates the situation. To address these issues, we propose a novel approach that utilizes contextual information between pointers, mimicking how human readers leverage contextual cues. Specifically, we develop a simple yet effective model comprising three components: individual pointer encoder, contextual modeling encoder, and readings decoder. The individual pointer encoder initially extracts text and vision features from each individual pointer, which are then integrated by the contextual modeling encoder to incorporate multimodal information and facilitate contextual modeling. Subsequently, the readings decoder leverages the contextually enriched representations to decode the accurate overall readings autoregressively. To facilitate research in this field, we construct a new dataset called WMeter5K. This is a multitask dataset that can be used not only for overall reading recognition but also for the detection and recognition of each digital wheel and pointer. Experimental results on WMeter5K show that our method outperforms prior arts by a large margin, highlighting the significance of contextual information in pointer meter recognition. The dataset and model are publicly available at https://github.com/ZZZHANG-jx/WMeter-Reader.