Named Entity Recogniton (NER) consists of tagging parts of an unstructured text containing particular semantic information. When applied to handwritten documents, it is possible to do it as a two-step approach in which Handwritten Text Recognition (HTR) is performed prior to tagging the automatic transcription. However, it is also possible to do both tasks simultaneously by using an HTR model that learns to output the transcription and the tagging symbols. In this paper, we focus on improving the one-step approach by introducing the auxiliary task of predicting Pyramidal Histograms of Characters (PHOC) in a Convolutional Recurrent Neural Network (CRNN) model. Moreover, given the recent rise of models that digest large amounts of data, we also study the usage of synthetic data to pretrain the proposed architecture. Our experiments show that by pretraining the PHOC-based architecture on synthetic data substantial improvements can be made in both transcription and tagging quality without compromising the computational cost of the decoding step. The resulting model matches the NER performance of the state-of-the-art while keeping its lightweight nature.