Classification of Historical Documents Based on LBP and LPQ Techniques

Abstract

Historical documents are important source for knowing culture, language, social activities, educational system, etc. The historical documents are in different languages and evolved over centuries and transformed to present modern language, classification of documents into various eras, recognition of words etc. In this paper, we have proposed a new approach to automatic identification of the age of the historical handwritten document images based on LBP (Local Binary Pattern) and LPQ (Local Phase Quantization) algorithm. The standard historical handwritten document images named as MPS (Medieval Paleographic Scale) dataset which is publicly available is used to experiment. LBP and LPQ descriptors are used to extract the features of the historical document images. Further, documents are classified based on the discriminating feature values using classifiers namely K-NN (K-Nearest Neighbors) and SVM (Support Vector Machine) classifier. The accuracy of historical handwritten document images by K-NN and SVM are 90.7% and 92.8% respectively.

Text-Independent Source Identification of Printed Documents using Texture Features and CNN Model

“…LBP is an efficient technique to capture the texture of the images. LBP is used to recognize blur faces and the age of the persons most efficiently [23] and identify dating of historical documents most efficiently [24]. Hence, we use this to extract word image features from each set of printer's document images separately.…”