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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/16768
Title: Profession identification using handwritten text images
Authors: Kumar P.
Gupta M.
Gupta M.
Sharma, Ambalika
Nain N.
Vipparthi S.K.
Raman B.
Published in: Proceedings of Communications in Computer and Information Science
Abstract: A writer handwriting depicts various information and it gives the insights into the physical, mental and emotional state of the writer. This art of analyzing and studying handwriting is graphology. The prime features of handwriting such as margins, slanted, the baseline can tell the characteristics of a writer. The writer handwriting analysis reveals strokes and patterns through which identification and understanding the personality of a writer is possible. The writing of a person molds into various shapes and styles, starting from school until the struggle for his/her career. If we examine the writings of a person from different stages of his/her life then we will see that there are many differences in the shapes, styles, and sizes of the characters. The proposed work analyze the handwriting data written by the writer’s from different professions and classify them based on the top features that characterize their profession. In this paper, the profession of a writer is identified by analyzing the features of writer’s offline handwritten images. The previous work mostly includes determining various traits like honesty, emotional stability of a writer. The Proposed work uses the CNN based model for the feature extraction from the writer’s offline handwritten images. © Springer Nature Singapore Pte Ltd 2020.
Citation: Proceedings of Communications in Computer and Information Science, (2020), 25- 35
URI: https://doi.org/10.1007/978-981-15-4018-9_3
http://repository.iitr.ac.in/handle/123456789/16768
Issue Date: 2020
Publisher: Springer
Keywords: Feature extraction
Handwritten document
Personality prediction
ISBN: 9.78981E+12
ISSN: 18650929
Author Scopus IDs: 57214054754
55465749900
57213189329
55482822100
Author Affiliations: Kumar, P., Indian Institute of Technology, Roorkee, Roorkee, India, National Institute of Technology Uttarakhand, Garhwal, Srinagar, India
Gupta, M., National Institute of Technology Uttarakhand, Garhwal, Srinagar, India
Gupta, M., National Institute of Technology Uttarakhand, Garhwal, Srinagar, India
Sharma, A., Indian Institute of Technology, Roorkee, Roorkee, India
Corresponding Author: Kumar, P.; National Institute of Technology Uttarakhand, Garhwal, India; email: parveen.cse@nituk.ac.in
Appears in Collections:Conference Publications [EE]

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