We introduce a novel application of handwriting recognition for Statistical Relational Learning. The proposed framework captures the intrinsic structure of handwriting by modeling fundamental character shape representations and their relationships using first-order logic. Our framework consists of three stages, 1 character extraction 2 feature generation and 3 class label prediction. In the character extraction stage, handwriting trajectory data is decoded into characters. Following this, character features predicates are defined across multiple levels - global, local and aggregated. Finally, a relational One-vs-All classifier is learned using relational functional gradient boosting RFGB. We evaluate our approach on two datasets and demonstrate comparable accuracy to a well-established, meticulously engineered approach in the handwriting recognition paradigm.