In this paper we explore different approaches for improving the performance of dependency models on discrete features for handwriting recognition. Hidden Markov Models have often been used for handwriting recognition. Conditional random fields (CRF?s) allow for more general dependencies and we investigate their use. We believe that this is the first attempt at apply CRF?s for handwriting recognition. We show that on the whole word recognition task, the CRF performs better than a HMM on a publicly available standard dataset of 20 pages of George Washington?s manuscripts. The scale space for the whole word recognition task is large - almost 1200 states. To make CRF computation tractable we use beam search to make inference more efficient using three different approaches. Better improvement can be obtained using the HMM by directly smoothing the discrete features using the collection frequencies. This shows the importance of smoothing and also indicates the difficulty of training CRF?s when large state spaces are involved.