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Deep neural networks-based offline writer identification using heterogeneous handwriting data: an evaluation via a novel standard dataset

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Abstract

In modern societies, handwritten identification is a great practical need and challenge for forensic sciences. Moreover, few research studies have focused on automatic offline handwritten document analysis. Also, there are very few standard datasets on handwritten document identification. In addition, handwritten documents lose their nature over time due to the spread and drying of ink. From this standpoint, the present study presents an offline writer identification system (in presence of the uncertainties such as different experimental conditions and environmental noises for more realistic assumptions) that is a critical need in forensic studies. For this purpose, a comprehensive right-to-left dataset is developed and devised by gathering data from 62 participants at different time intervals under different experimental conditions. This dataset is designed based on American Society for Testing and Materials (ASTM) standards. A Deep Convolutional Neural Network (DCNN) model based on modified pre-trained networks is designed and developed to extract features from raw data hierarchically. The proposed DCNN model is tested and investigated not only on the designed dataset but also on different datasets. One notable advantage of the present study is that it has made use of heterogeneous data. Another remarkable aspect of the proposed study is that the proposed DCNN model is independent of any specific languages which can be applied on various languages. The results of the study indicate that the proposed DCNN model can learn features hierarchically from the handwriting raw data and achieve higher accuracy than other comparative methods.

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Authors and Affiliations

  1. Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, 57169-63896, Urmia, Iran

    Seyed Nadi Mohamed Khosroshahi, Seyed Naser Razavi, Amin Babazadeh Sangar & Kambiz Majidzadeh

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, 51666-16471, Tabriz, Iran

    Seyed Naser Razavi

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  1. Seyed Nadi Mohamed Khosroshahi
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Khosroshahi, S.N.M., Razavi, S.N., Sangar, A.B. et al. Deep neural networks-based offline writer identification using heterogeneous handwriting data: an evaluation via a novel standard dataset. J Ambient Intell Human Comput 13, 2685–2704 (2022). https://doi.org/10.1007/s12652-021-03253-2

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