We present a strategy, called Seq2Seg, to reach both precise and accurate recognition and segmentation for children handwritten words. Reaching such high performance for both tasks is necessary to give personalized feedback to children who are learning how to write. The first contribution is to combine the predictions of an accurate Seq2Seq model with the predictions of a R-CNN object detector. The second one is to refine the bounding box predictions provided by the detector with a segmentation lattice computed from the online signal. An ablation study shows that both contributions are relevant, and their combination is efficient enough for immediate feedback and achieves state of the art results even compared to more informed systems.