The generation of word hypotheses for segmentation-free word spotting on document level is usually subject to heuristic expert design. This involves strong assumptions about the visual appearance of text in the document images. In this paper we propose to generate hypotheses with text detectors. In order to do so, we present three detectors that are based on SIFT contrast scores, CNN region classification scores and attribute activation maps. The uncertainty in the detector scores is modeled with the extremal regions method. Retrieving word hypotheses is based on PHOC representations which we compute with the TPP-PHOCNet. We evaluate our method on the George Washington dataset and the ICFHR 2016 KWS competition benchmarks. In the evaluation we show that high word detection rates can be achieved. This is a prerequisite for high retrieval performance that is competitive with the state-of-the-art.