The label texts on food package include some information like production date, nutrition facts and production corporation etc. The information provides important foundation for consumers to buy food. It also can help the food supervision and inspection administrations to discover the potential problems of food safety. Food label detection is the groundwork of food label recognition. It can help to decrease the heavy workload of manual inputting and advance efficiency of data processing. The dataset of food label was constructed firstly, and then a semantic segmentation based distance field model (DFM) was proposed. In DFM two tasks were included: pixel classification and distance field regression. The pixel classification task was used to segment the text from background regions, and the distance field regression task was used to predict the normalized distance from the pixel located in the text region to the boundary of text region. For effectively using the correlation of two tasks, an attention module was added into DFM to optimize the model structure. In addition, the loss function was improved to resolve the loss value of the distance field regression as it was too small to train smoothly. The results of ablation experiment showed that the accuracy of the proposed model was increased by 4.39 percentage points and 3.80 percentage points respectively according to the improvement of attention module and loss function. The comparative experiments of different model methods showed that DFM had good performance in detecting the text of food labels, and the recall rate and precision were 87.61% and 76.50%, respectively.