Neural Networks for Handwritten English Alphabet Recognition

Perwej, Yusuf; Chaturvedi, Ashish

doi:10.5120/2449-2824

Cited by 38 publications

(11 citation statements)

References 12 publications

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“…In this work, we have used neural network (NN) [16] and random forest (RF) [8] classifiers to determine the recognition accuracy of roman characters.…”

Section: Classifiers For Character Recognitionmentioning

confidence: 99%

Recognition of Roman Characters using Geometric and Regional Features

Verma*¹,

Agarwal²,

Aggarwal³

2019

IJRTE

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This paper presents the Roman Characters Recognition in clean and noisy environment using geometrical and regional features. The geometrical and regional features are extracted from the standard dataset and are combined to achieve better recognition. The combination of these features is classified using neural network (NN) and random forest (RF) classifiers. In our experiments, we have achieved the recognition accuracy of 100% for some characters. However, the average recognition accuracy of 85.7% has been recorded by using NN and 88% has been recorded by using RF classifier, respectively

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“…In this work, we have used neural network (NN) [16] and random forest (RF) [8] classifiers to determine the recognition accuracy of roman characters.…”

Section: Classifiers For Character Recognitionmentioning

confidence: 99%

Recognition of Roman Characters using Geometric and Regional Features

Verma*¹,

Agarwal²,

Aggarwal³

2019

IJRTE

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“…The proposed system can achieve remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. Yusuf Perwej et al [7] express the use of neural networks for developing a system that can recognize handwritten English alphabets. Each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is given to our neural network system for recognition.…”

Section: Literature Survey a Suman Avdhesh Yadav Proposed A Systmentioning

confidence: 99%

Review of methods for Handwritten Character Identification using Optical Character Recognition (OCR)

Gurav¹

2019

IJRASET

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It is very hard to recognize handwritten character using optical character recognition. Also it is very difficult to convert handwritten image into text format. Optical character recognition has become one of the most successful applications of technology in the field of pattern recognition and artificial intelligence. OCR is a widespread technology to recognize text inside images, such as scanned documents and photos. This paper gives the survey of different techniques used by the researcher for dynamic pattern and character recognition.

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“…The high sensitivity of TENG toward tiny vibration, the cost-effective fabrication process, and the flexible device structure provide much possibilities for the smart and interesting applications, such as humidity sensor, [10] pressure sensor, [11] active acoustic sensor, [12] ultraviolet detector, [13] Keystroke dynamics identification, [14] handheld printer, [15] and so on.Machine learning (ML) [16,17] is usually referred to statistical models and algorithms used by computer systems to conduct a specific task without too much complex instructions. The high sensitivity of TENG toward tiny vibration, the cost-effective fabrication process, and the flexible device structure provide much possibilities for the smart and interesting applications, such as humidity sensor, [10] pressure sensor, [11] active acoustic sensor, [12] ultraviolet detector, [13] Keystroke dynamics identification, [14] handheld printer, [15] and so on.…”

mentioning

confidence: 99%

“…Machine learning (ML) [16,17] is usually referred to statistical models and algorithms used by computer systems to conduct a specific task without too much complex instructions. ML algorithms are widely used in computer vision, disease diagnosis, email filtering, and signal recognition.…”

mentioning

confidence: 99%

Triboelectric Nanogenerator Based Smart Electronics via Machine Learning

Zhao

et al. 2020

Adv Materials Technologies

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a conjunction of triboelectric effect and electrostatic induction. The high sensitivity of TENG toward tiny vibration, the cost-effective fabrication process, and the flexible device structure provide much possibilities for the smart and interesting applications, such as humidity sensor, [10] pressure sensor, [11] active acoustic sensor, [12] ultraviolet detector, [13] Keystroke dynamics identification, [14] handheld printer, [15] and so on.Machine learning (ML) [16,17] is usually referred to statistical models and algorithms used by computer systems to conduct a specific task without too much complex instructions. ML algorithms are widely used in computer vision, disease diagnosis, email filtering, and signal recognition. ML has close relationship with computational statistics focusing on making predictions using computers. Support vector machine (SVM) [18] is a supervised ML algorithm for linear and nonlinear classification. The SVM training algorithm builds a model based on functional margin, which generates a line or a hyperplane to separate the data into classes. In information theory and ML, by obtaining a set of principal variables, dimension reduction can reduce the dimension of random variables. t-distributed stochastic neighbor embedding (t-SNE) [19] is a approach to reduce high-dimensional datasets and widely used in speech processing, genomic data, natural language processing, and image processing. t-SNE starts by calculating the probability of high-dimensional space points similarity as well as the probability of the corresponding low-dimensional space points similarity to map the multi-dimensional data to a lower dimensional space.With the development of artificial intelligence, it is urgent to empower traditional electronics with the ability to "think," to "analyze," and to "advise." Here, a new product concept namely triboelectric nanogenerator (TENG) based smart electronics via the automatic machine learning data analysis algorithm is proposed. In this work, a simple water processing technique is used to fabricate porous polydimethylsiloxane, together with the weaving copper mesh, forming a high sensitivity flexible TENG. The as-prepared TENG presents high sensitivity for the voice signal and handwriting signal detection with ≈0.2 V amplitude in the common talking and writing condition. Three words' pronunciation are recorded and the ensemble method is used as the machine learning model for the voice signal recognition with a recognition accuracy of 93.3%. To further demonstrate the possibility of applying machine learning algorithm for automatic analysis and recognition, larger database is analyzed. Twenty-six letters' handwriting signals with total 520 samples are collected and a letter fingerprint library is established for further analysis. Hierarchical clustering and similarity matrix are used to study the intrinsic relationship between letters. "Medium Gaussian support vector machine" is used as machine learning model for the 26-letter fingerprint identification with recognition accuracy of...

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Neural Networks for Handwritten English Alphabet Recognition

Cited by 38 publications

References 12 publications

Recognition of Roman Characters using Geometric and Regional Features

Recognition of Roman Characters using Geometric and Regional Features

Review of methods for Handwritten Character Identification using Optical Character Recognition (OCR)

Triboelectric Nanogenerator Based Smart Electronics via Machine Learning

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