28, May 2026

Printed Document Age Classification using Hybrid HOG and GLCM Handcrafted Features

VOLUME-12; ISSUE-5; MAY-2026

Author(s): Dr. Pushpalata Gonasagi

Authors Affiliations:

Associate Professor, Department of Computer Science,

Govt. First Grade College, Mahagaon Cross, Kalaburagi, India

DOIs:10.2015/IJIRMF/202605027     |     Paper ID: IJIRMF202605027


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Document age classification is an important task in forensic document examination, archival preservation, and document authentication. Variations in printing style, ink texture, paper quality, and aging effects provide useful information for determining the approximate age of printed documents. Traditional document age identification methods mainly depend on handcrafted texture descriptors combined with machine learning classifiers for feature analysis and classification. This paper presents a handcrafted feature-based framework for printed document age classification using Histogram of Oriented Gradients (HOG) and Gray Level Co-occurrence Matrix (GLCM) texture features with Support Vector Machine (SVM) classification. The proposed system extracts structural and texture information from document images to identify aging characteristics associated with different decades. The dataset consists of Life Magazine cover page images collected from five decades: 1930, 1940, 1950, 1960, and 1970. The methodology includes dataset collection, preprocessing, handcrafted feature extraction, feature fusion, and SVM classification. During preprocessing, grayscale conversion, resizing, filtering, normalization, and augmentation are performed to improve feature quality. HOG features capture edge and gradient information, while GLCM features extract statistical texture properties such as contrast, correlation, energy, and homogeneity. The extracted features are combined and classified using a multiclass SVM classifier. Experimental results demonstrate that the proposed hybrid handcrafted feature approach achieves an accuracy of 91.6%, outperforming several traditional feature extraction methods. The proposed framework provides an efficient and reliable solution for forensic document age classification applications.

Document Age Classification, HOG, GLCM, Handcrafted Features, SVM, Document Authentication.

Dr. Pushpalata Gonasagi (2026); Printed Document Age Classification using Hybrid HOG and GLCM Handcrafted Features, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-12, Issue-5, Available on –   https://www.ijirmf.com/

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