Discrimination of Machine-Printed from Handwritten Text Using Simple Structural Characteristics

In this paper, we present a trainable approach to discriminate between machine-printed and handwritten text. An integrated system able to localize text areas and split them in text-lines is used. A set of simple and easyto-compute structural characteristics that capture the differences between machine-printed and handwritten text-lines is introduced. Experiments on document images taken from IAM-DB and GRUHD databases show a remarkable performance of the proposed approach that requires minimal training data.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Efstathios Stamatatos, Ergina Kavallieratou
• Keywords: Computer Vision, Handwritten Text, ICPR 2004, Minimal Training Data, Trainable Approach