This paper presents a detailed view of a novel solution to the computer transcription of handwritten Pitman?s shorthand as a means of rapid text entry (up to 100 words per minute) into today?s handheld devices with the use of a Bayesian network representation. Detailed design considerations of Bayesian network based shorthand outline models, including hypothesis of missing vowel components occurring in speed writing and unclear thickness and length of electrical pen-strokes are presented, along with graphical examples. Although Pitman?s shorthand is written phonetically, our outline models are also based on low-level geometric attributes rather than phonetic attributes with the intention of coping with the unique features of handwritten Pitman?s shorthand. The experimental results indicate an average accuracy of 92.86%, which is a marked improvement over previous applications of the same framework.