We propose Bayesian background models for keyword spotting in handwritten documents. We cover a detailed illustration of two of the proposed Bayesian background models. The Bayesian formulation adds uncertainty to handle variation in writing styles. The weights learned on the individual samples provide better rejection criteria. A line level approach that avoids any error is introduced by the word segmentation.