Ensemble classifier‐based off‐line handwritten word recognition system in holistic approach

Abstract

This study presents a novel ensemble classifier-based off-line handwritten word recognition system following a holistic approach. Here each handwritten word is recognised using two handcrafted features, namely (i) Arnold transform-based feature that addresses local directional feature which depends on the stroke orientation distribution of cursive word and (ii) oriented curvature-based feature which is basically the histogram of curvelet index and one machine generated feature using deep convolution neural network (DCNN). In this study, a new architecture of DCNN is proposed for handwritten word recognition. These features are recognised by three classifiers separately. Finally, the decision of three classifiers is combined to predict the ultimate word class level. To fuse the decision of individual classifiers, the authors have explored three strategies: (i) vote for strongest decision, (ii) vote for majority decision and (iii) vote for the sum of the decisions. The proposed handwritten word recognition system is tested on three handwritten word databases: (i) CENPARMI database, (ii) IAM database and (iii) ISIHWD database. The performance of the proposed system is promising and comparable to state-of-the-art handwriting recognition systems.