Review of offline handwritten text recognition in south Indian languages

Anju, A. T.; Chacko, Binu P.; Basheer, K. P. Mohammad

doi:10.26637/mjm0901/0132

Cited by 3 publications

(1 citation statement)

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“…27, No. 3, September 2022: 1681-1688 1682 approaches were utilized to Pashto handwritten character recognition [9]. The power of deep neural networks (DNN) is for providing effectual implementation with no assuming the basic rule of deep element is linear [10].…”

Section: Introductionmentioning

confidence: 99%

Puzzle optimization algorithm with capsule networks based Telugu character recognition model

Phaniram

Reddy

2022

IJEECS

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Recently, <span>Telugu character recognition (TCR) becomes a hot research topic because of drastic increase in technological advancements such as multimedia, and smartphones. Though numerous works have been concentrated on offline TCR models, it is still needed to develop automated and intelligent online TCR models. This paper presents a novel puzzle optimization algorithm with capsule networks-based Telugu character recognition (POACN-TCR) model. The presented POACN-TCR model intends to effectively identify and recognize distinct Telugu characters online. To accomplish this, the POACN-TCR model primarily undergoes pre-processing in different ways such as normalization, smoothing, and interpolation. In addition, the POACN-TCR model designs an effective capsule network (CapsNet) model to generate feature vectors and hyperparameter optimization takes place using puzzle optimization algorithm (POA). Finally, the C4.5 decision tree classifier is utilized for the effectual recognition of Telugu characters. The utilization of POA for hyperparameter optimization of the CapsNet model helps in achieving improved recognition performance. For ensuring the enhanced outcomes of the POACN-TCR model, a wide-ranging experimental analysis is performed and the outcomes pointed out the betterment of the POACN-TCR model on the recent approaches.</span>

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Section: Introductionmentioning

confidence: 99%