The detection of baselines of text is a necessary pre-processing step for many modern methods of automatic handwriting recognition. In this work a two-stage system for the automatic detection of text baselines of handwritten text is presented. In a first step pixel-wise segmentation on the document image is performed to classify pixels as baselines, start points and end points. This segmentation is then used to extract the start points of lines. Starting from these points the baseline is extracted using a recurrent convolutional neural network that directly outputs the baseline coordinates. This method allows the direct extraction of baseline coordinates as the output of a neural network without the use of any post processing steps. The model is evaluated on the cBAD dataset from the ICDAR 2019 competition on baseline detection.