Handwritten character recognition using diagonal-based feature extraction

Vijayalakshmi, K.; Aparna, S.; Gopal, Gayatri; Hans, W. Jino

doi:10.1109/wispnet.2017.8299949

Cited by 6 publications

(9 citation statements)

References 4 publications

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“…The greater the value of complexity parameter the greater will be the shrinkage and hence making coefficients healthy and robust to collinearity. [20] • Bernoulli Naïve Bayes:…”

Section: Methodsmentioning

confidence: 99%

“…While in the offline recognition, only the writings from the images captured by the scanner are used, and this is the reason why online recognition works better. But offline recognition has its own applications and hence is an active area of development [5]. So, the data can be collected using both these methods.…”

Section: Architecturementioning

confidence: 99%

“…Sinha and Mahabala [20] proposed a system in their research in which they described the relations between characters of the script. Their research demonstrated how understanding the association between symbols can help in understanding and recognizing the characters.…”

Section: Feature Extraction Techniquesmentioning

confidence: 99%

“…A variety of classifiers and techniques like ANN [18], [19], HMM [20], fuzzy rules, rough sets [8], etc. have been used by researchers for the purpose of classification and recognition in the past.…”

Section: Recognition Techniquesmentioning

confidence: 99%

“…They used neural network classifiers for recognition classifying printed characters and words [23]. In contrast to this, Jawahar et al [20] used support vector machines for the same purpose. In this, they used 5 filters to process the image.…”

Section: Recognition Techniquesmentioning

confidence: 99%

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Deep Learning Based Real Time Devanagari Character Recognition

Chhabra¹

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The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are many algorithms and models that can be used for this purpose. This project tries to implement and optimize a deep learning-based model which will be able to recognize Devanagari script's characters in real time by analyzing the hand movements.

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“…The greater the value of complexity parameter the greater will be the shrinkage and hence making coefficients healthy and robust to collinearity. [20] • Bernoulli Naïve Bayes:…”

Section: Methodsmentioning

confidence: 99%

Section: Architecturementioning

confidence: 99%

Section: Feature Extraction Techniquesmentioning

confidence: 99%

Section: Recognition Techniquesmentioning

confidence: 99%

Section: Recognition Techniquesmentioning

confidence: 99%

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Deep Learning Based Real Time Devanagari Character Recognition

Chhabra¹

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Effective Survey on Handwriting Character Recognition

Monisha¹,

Malathi²

2020

Computational Methods and Data Engineering

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Ancient Character Recognition: A Comprehensive Review

Krithiga,

Varsini,

Gabriel Joshua

et al. 2023

IEEE Access

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Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil letters (Vattezhuthu) into modern-day Tamil letter structures. Various methodologies and pre-processing techniques have been used for denoising the ancient Tamil manuscript to retrieve the Tamil text. Researchers have used various synthesized and scanned images of stone wall inscriptions, palm leaves manuscripts, and offline handwritten characters for their analysis. Over the years, Ancient Tamil scripts have deteriorated with time due to various natural calamities. Strong denoising and feature extraction methods are required to separate the letters accurately to tackle this issue. Techniques such as CNN(OCR), ResNet, SVM, KNN, HorVer method, etc., are utilized to digitize Tamil characters. This technique has successfully converted handwritten characters into digitalized text for multiple languages, including Tamil, Arabic, English, Latin, Chinese, German, etc. Different models have been evaluated based on their segmentation and recognition rates, accuracy, detection rate, precision, and confusion matrix. This paper will concentrate on Ancient Tamil character segmentation and recognition models. Besides, we will give an overview of the different models and datasets available. Lastly, we summarise the key challenges and the future scope related to the topic.

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Handwritten character recognition using diagonal-based feature extraction

Cited by 6 publications

References 4 publications

Deep Learning Based Real Time Devanagari Character Recognition

Deep Learning Based Real Time Devanagari Character Recognition

Effective Survey on Handwriting Character Recognition

Ancient Character Recognition: A Comprehensive Review

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