New Deep Spatio-Structural Features of Handwritten Text Lines for Document Age Classi¯cation

Article Type

Research Article

Publication Title

International Journal of Pattern Recognition and Artificial Intelligence


Document age estimation using handwritten text line images is useful for several pattern recognition and arti¯cial intelligence applications such as forged signature veri¯cation, writer identi¯cation, gender identi¯cation, personality traits identi¯cation, and fraudulent document identi¯cation. This paper presents a novel method for document age classi¯cation at the text line level. For segmenting text lines from handwritten document images, the wavelet decomposition is used in a novel way. We explore multiple levels of wavelet decomposition, which introduce blur as the number of levels increases for detecting word components. The detected components are then used for a direction guided-driven growing approach with linearity, and nonlinearity criteria for segmenting text lines. For classi¯cation of text line images of di®erent ages, inspired by the observation that, as the age of a document increases, the quality of its image degrades, the proposed method extracts the structural, contrast, and spatial features to study degradations at di®erent wavelet decomposition levels. The speci¯c advantages of DenseNet, namely, strong feature propagation, mitigation of the vanishing gradient problem, reuse of features, and the reduction of the number of parameters motivated us to use DenseNet121 along with a Multi-layer Perceptron (MLP) for the classi¯cation of text lines of di®erent ages by feeding features and the original image as input. To demonstrate the e±cacy of the proposed model, experiments were conducted on our own as well as standard datasets for both text line segmentation and document age classi¯cation. The results show that the proposed method outperforms the existing methods for text line segmentation in terms of precision, recall, F-measure, and document age classi¯cation in terms of average classi¯cation rate.



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