Handwritten Off-line Kannada Character/Word Recognition Using Hidden Markov Model

Veena, G; Kumar, T. N. R.; Sushma, A.

doi:10.1007/978-981-10-5146-3_33

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The traditional recognition steps are generally divided into three steps: preprocessing, feature extraction, and character classification [4]. Among the relatively effective character classification methods are Support Vector Machine (SVM) [5], K-nearest [6,7], Modified Quadratic Discriminant Function (MQDF) [8], Hidden Markov Model (HMM) [9,10] and so on. However, traditional methods are ineffective in recognition tasks due to their limited ability to extract features.…”

Section: Introductionmentioning

confidence: 99%

Four-channel convolutional Chinese handwriting recognition based on MobileNetV2

Chen,

Li,

et al. 2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

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Section: Introductionmentioning

confidence: 99%

Four-channel convolutional Chinese handwriting recognition based on MobileNetV2

Chen,

Li,

et al. 2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

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Text-Line Extraction from Historical Kannada Document

Ravi¹,

Naveena²,

Kumar

et al. 2019

Frontiers in Intelligent Computing: Theory and Applications

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Comparative Study of Handwritten Character Recognition System for Indian Languages

Patel

Parikh

Patel

2021

ICT With Intelligent Applications

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Handwritten Character Recognition is the emerging domain for the research in image processing and pattern recognition because a huge amount of handwritten and OCR documents need to be processed in daily needs of different systems. Based on surveys of Human Development very less amount of Indian population can write and read English language. It specifies the need for research in Indian script for OCR technology. The research in this area is actually needed for developing countries such as India and remains beneficial to inspire the human-machine interface system. HCR makes it ease the automatic processing of tax and admission forms, bank cheques, addresses and postal codes. In the comparison of printed character recognition, handwritten character recognition is difficult because it can be different from person to person based on the style of writing, curve, size, strokes and thickness of character. Sufficient research work has been performed in foreign handwritten characters such as Arabic, Chinese and Japanese but very less research work has been performed in Indian scripts. This paper provides a comparative study of famous and well-known Tamil, Telugu, Kannada and Gujarati Indian scripts. This paper provides a detailed study based on the used methodology, dataset and accuracy.

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Handwritten Off-line Kannada Character/Word Recognition Using Hidden Markov Model

Cited by 3 publications

References 16 publications

Four-channel convolutional Chinese handwriting recognition based on MobileNetV2

Four-channel convolutional Chinese handwriting recognition based on MobileNetV2

Text-Line Extraction from Historical Kannada Document

Comparative Study of Handwritten Character Recognition System for Indian Languages

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