NEURAL NETWORKBASED CONTEXT DRIVEN RECOGNITION OF ONLINE CURSIVE SCRIP
Predrag Neskovic, Leon N. Cooper · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2004
Most of the stateoftheart systems for cursive script recognition are based on a combination of neural networks (NN) and hidden Markov models (HMMs) 1;2 . The postprocessing stage is almost exclusively modeled using HMMs and the dynamic programming (DP) technique (the Viterbi algorithm) is used to efficiently search the space of possible segmentations. In this work we introduce a neural networkbased model for representing handwritten patterns as an alternative to HMMs. In addition, we present a new algorithm that uses context information to segment, modify and organize bottom up information in order to achieve successful recognition.