Background: The growing use of smart hand-held devices in the daily lives of the people
urges for the requirement of online handwritten text recognition. Online handwritten text recognition
refers to the identification of the handwritten text at the very moment it is written on a digitizing
tablet using some pen- like stylus. Several techniques are available for online handwritten text
recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research
is available for Indic scripts.
Objective: This article presents a novel approach for online handwritten numeral and character (simple
and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil.
Methods: The proposed work employs the zone wise slopes of dominant points (ZSDP) method for
feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov
Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved
by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer
theory. The system is trained using separate as well as combined dataset of numerals, simple and
Results: The performance of the present system is evaluated using large self-generated datasets as
well as public datasets. Results obtained from the present work demonstrate that the proposed system
outperforms the existing works in this regard.
Conclusion: This work will be helpful to carry out researches on online recognition of handwritten
character in other Indic scripts as well as recognition of isolated words in various Indic scripts including
the scripts used in the present work.
Keywords: Online handwriting, Character recognition, Indic scripts, Zone-wise feature extraction, SVM, HMM combination
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