Finite State Machine Based Decoding of Handwritten Text Using Recurrent Neural Networks
Cuong Tuan Nguyen, Masaki Nakagawa · 2016
This paper presents a Finite State Machine (FSM) to reduce user's waiting time to get the recognition result after finishing writing in recognition of online handwritten English text. The lexicon is modeled by a FSM, and then determination and minimization are applied to reduce the number of states. The reduction of states in the FSM shortens the waiting time without degrading the recognition accuracy. Moreover, by merging incoming paths to each state, the recognition rate is improved. The N-best states decoding method also reduces the waiting time significantly with small degradation in recognition accuracy. Experiments on IAM-OnDB and IBM_UB_1 show the effectiveness of the method in both reducing waiting and improving recognition accuracy.