Multiple research initiatives have been reported to yield highly effective results for the text detection problem. However, most of those solutions are very costly, which hamper their use in several applications that rely on the use of devices with restrictive processing power, like smartwatches and mobile phones. In this paper, we address this issue by investigating the use of efficient object detection networks for this problem. We propose the combination of two light architectures, MobileNetV2 and Single Shot Detector (SSD), for the text detection problem. Experimental results in the ICDAR'11 and ICDAR'13 datasets demonstrate that our solution yields the best trade-off between effectiveness and efficiency and also achieved the state-of-the-art results in the ICDAR'11 dataset with an f-measure of 96.09%.