Text and non-text separation in handwritten document images using local binary pattern operator

Abstract

Development of an automated system for handwritten document analysis is being considered as an important research topic since last few decades. Digitized documents, either handwritten or printed, contain a mixture of text and non-text elements which need to be separated for designing a document layout analyzer or even an Optical Character Recognizer. In this paper, a technique is described to separate the text objects from the non-text objects present in a handwritten document image. For this purpose, a Rotation Invariant Local Binary Pattern (RILBP) based texture feature is used to represent the said components, at the feature space. Finally, the classification is carried out using an Artificial Neural Network based classifier called, Multi-layer Perceptron (MLP). The system provides an impressive result on a database comprising of 100 handwritten document images.