Abstract
Most research on document analysis is carried out through human intervention, such as manually counting the parameters of document analysis as the input of neural networks. Document analysis via computer vision methods is a relatively less explored area of research.
In this paper, we investigated the literature on document analysis in recent years, and summarized its development process and the commonly used research methods, discussed their advantages and disadvantages. Meanwhile, we put forward the general research ideas and steps for document analysis, highlight the limitations of existing processes and the challenges typically faced when designing such systems, provide potential, feasible solutions, and point out the direction of further research, which give guidance to novice researchers and have reference value for subsequent researchers. Our experiments verify that our method is feasible and can be used as a substitute for specific application scenarios without professional handwriting experts.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kedar, S., Nair, V., Kulkarni, S.: Personality identification through handwriting analysis: a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng., 5(1), 548–556 (2015)
Shengxi, X.: Graphology. Fudan University Law School, Shanghai (1943)
Yang, G., Lin, B.: Chinese Characters and Personality: an Exploratory Study, PhD dissertation of Department of Psychology, National Taiwan University (1964)
Weng, S.: Study on Chinese Handwriting and Personality. Bulletin of the Department of Chinese Literature National Chengchi Univesity, (2), pp. 146–160 (1981)
Yuan, Z.: Research and application on handwriting. Beijing Mass press (1993)
Survey on handwriting-based personality trait identification: K. Chaudhari and A. Thakkar. Expert Syst. Appl. 124, 282–308 (2019)
Gavrilescu, M., Vizireanu, N.: Predicting the Big Five personality traits from handwriting. EURASIP J. Image Video Process.57(1), 1–17 (2018)
Chen, Z., Lin, T.: Automatic personality identification using writing behaviours: an exploratory study. Behaviour Info. Technol. 36(8), 839–845 (2017)
Garoot, A., Safar, M., Nobile, N.: Computational graphology applied to handwriting images. In: Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence. Montreal, Canada, pp. 677–682 (2018)
Chaubey, G., Arjaria, S.K.: Personality Prediction Through Handwriting Analysis Using Convolutional Neural Networks. Tiwari, R., Mishra, A., Yadav, N., Pavone, M. (eds) In: Proceedings of International Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-3802-2_5
Nair, G.H., Rekha, V., Soumya Krishnan, M.: Handwriting Analysis Using Deep Learning Approach for the Detection of Personality Traits. In: Karuppusamy, P., Perikos, I., García Márquez, F.P. (eds) Ubiquitous Intelligent Systems. Smart Innovation, Systems and Technologies, vol 243. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-3675-2_40
Anari, M.S., Rezaee, K., Ahmadi, A.: TraitLWNet: a novel predictor of personality trait by analyzing Persian handwriting based on lightweight deep convolutional neural network. Multimedia Tools Appl. 81(8), 10673–10693 (2022). https://doi.org/10.1007/s11042-022-12295-3
Sudholt, S., Fink, G.A.: PHOCNet: A deep convolutional neural network for word spotting in handwritten documents. In: 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Shenzhen, China, pp. 277–282 (2016)
Yin, F., Wang, Q.F. Zhang, X.Y. et al.: Chinese handwriting recognition competition (ICDAR)Washington D.C., USA, pp. 1464–1469 (2013)
Liu, C.-L., Yin, F., Wang, D.-H., et al.: Chinese Handwriting Database Building and Benchmarking. Adv. Chin. Doc. Text Proc. 2, 31–55 (2017)
Xu, Y., Yin, F., Wang, D.-H., et al.: CASIA-AHCDB: a Large-scale Chinese Ancient Handwritten Characters Database. International Journal on Document Analysis and Recognition (IJDAR),Sydney. Australia, pp. 793–798 (2019)
Ahmed, P., Mathkour, H.: On The Development of an Automated Graphology System. In: Proceedings of the 2008 International Conference on Artificial Intelligence (IC-AI) Las Vegas, USA, pp. 897–901 (2008)
Fallah, B., Khotanlou, H.: Identify human personality parameters based on handwriting using neural network. In Artificial Intelligence and Robotics (IRANOPEN). Qazvin, Iran, pp. 120–126 (2016)
Chaudhuri, A., Mandaviya, K., Badelia, P.: Optical character recognition systems. In: Optical Character Recognition Systems for Different Languages with Soft Computing, pp. 9–41. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50252-6_2
Bal, A., Saha, R.: An improved method for handwritten document analysis using segmentation, baseline recognition and writing pressure detection. In: 6th International Conference on Advances in Computing & Communications (ICACC) Cochin, India, pp. 403–415 (2016)
Hashemi, S., Vaseghi, B., Torgheh, F.: Graphology for Farsi handwriting using image processing techniques. IOSR J. Electron. Commun. Eng. (IOSR-JECE), 10(3), 1–7 (2015)
Norman, W. T.: Toward an adequate taxonomy of personality attributes: replicated factor structure in peer nomination personality ratings. J. Abnorm. Soc. Psychol. 66(6), 574–583(1963)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Y., Tang, Y., Suen, C.Y. (2022). Review of Handwriting Analysis for Predicting Personality Traits. In: Krzyzak, A., Suen, C.Y., Torsello, A., Nobile, N. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2022. Lecture Notes in Computer Science, vol 13813. Springer, Cham. https://doi.org/10.1007/978-3-031-23028-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-23028-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23027-1
Online ISBN: 978-3-031-23028-8
eBook Packages: Computer ScienceComputer Science (R0)