Abstract
Handwriting recognition, in the current scenario is becoming an important task to recognize ones general identity by investing minimum time for it. As handwriting recognition plays a very significant role for authentication of documents, genuineness of historical manuscripts and many more. It is now becoming an exploiting field of research as one can copy others handwriting but can never write the same in an identical manner. From here comes rise of a new field of research for researchers. Now this handwriting recognition concept is being used for classifying gender, age, handedness, ethnicity and many more aspects just by analyzing written data of the writers. The focus for this exploration is to study and summarize various techniques used so far for analyzing the handwriting and recognizing the possible features of a person. This concept has built up its importance by raising its usage in various fields such as; for forensic scrutiny, identification of writers, word perceiving, verification of signature etc. The advancement of this research for this concept has increased by observing the attractive positive results for its outcomes and the eagerness of building a computerized system for processing handwriting so that it could replace the manual analysis. From prediction till recognition of correct identity of the writers’, this complete process takes place majorly in three basic steps: segmentation, features extractions and classification. SVM, KNN and RFC supervised machine learning algorithms are being used for classification purpose. Few of plenty of researches done by researchers for the same purpose had also purposed approaches by using the unsupervised machine learning techniques as well. Thus, both the phenomenon may be utilized for fulfilling the defined query with most appropriate possible computational approach.
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Agarwal, A., Saraswat, M. (2022). Analyzing Various Handwriting Recognition Phenomenon for Predicting Gender, Age and Handedness. In: Sugumaran, V., Upadhyay, D., Sharma, S. (eds) Advancements in Interdisciplinary Research. AIR 2022. Communications in Computer and Information Science, vol 1738. Springer, Cham. https://doi.org/10.1007/978-3-031-23724-9_21
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