Generation of Synthetic Training Data for an HMM-based Handwriting Recognition System

A perturbation model for generating synthetic textlines from existing cursively handwritten lines of text produced by human writers is presented. Our purpose is to improve the performance of an HMM-based off-line cursive handwriting recognition system by providing it with additional synthetic training data. Two kinds of perturbations are applied, geometrical transformations and thinning/thickening operations. The proposed perturbation model is evaluated under different experimental conditions.

• Type: Conference
• Year: 2003
• Where: ICDAR
• Authors: Tamás Varga, Horst Bunke
• Keywords: Additional Synthetic Training, Document Analysis, ICDAR 2003, Off-line Cursive Handwriting, Perturbation Model