Recognizing images of handwritten digits using learning vector quantization artificial neural network

Khatri, Sunil Kumar; Dutta, Shivali; Johri, Prashant

doi:10.1109/icrito.2015.7359307

Cited by 6 publications

(8 citation statements)

References 4 publications

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“…The network is created on the basis of necessary parameters. [11]. The number of input neurons is 18 according to the number of feature extractions.…”

Section: Identification Of Varieties Using Learning Vector Quantizamentioning

confidence: 99%

Identification of Rice Variety Using Geometric Features and Neural Network

Srimulyani¹,

Musdholifah

2019

Indonesian J. Comput. Cybern. Syst.

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Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.

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“…The network is created on the basis of necessary parameters. [11]. The number of input neurons is 18 according to the number of feature extractions.…”

Section: Identification Of Varieties Using Learning Vector Quantizamentioning

confidence: 99%

Identification of Rice Variety Using Geometric Features and Neural Network

Srimulyani¹,

Musdholifah

2019

Indonesian J. Comput. Cybern. Syst.

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“…It also compare them with traditional recognition methods which are MRF and MQDF by carrying out some experiments on CROHME database [5]. Khatri et al (2015) presented a paper which is based on supervised learning vector quantization neural network categorized under artificial neural network. The images of digits are recognized, trained and tested.…”

Section: Related Workmentioning

confidence: 99%

“…The images of digits are recognized, trained and tested. After the network is created digits are trained using training dataset vectors and testing is applied to the images of digits which are isolated to each other by segmenting the image and resizing the digit image accordingly for better accuracy [6]. presented offline handwritten character recognition of English alphabets using a three layered feed forward neural network.…”

Section: Related Workmentioning

confidence: 99%

Identification of Handwritten Complex Mathematical Equations

Shinde¹,

Khanna²,

Waghulade³

2019

IJIGSP

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The mathematical notation is well known and used throughout the world. Humanity has evolved from simple methods to represent accounts to the current formal notation capable of modeling complex problems. In addition, mathematical equations are a universal language in the scientific world, and many resources such as science and engineering technology, medical field also not an exception containing mathematics have been created during the last decades. However, to efficiently access all that information, scientific documents must be digitized or produced directly in electronic formats. Although most people are able to understand and produce mathematical information, introducing mathematical equations into electronic devices requires learning special notations or using editors. The proposed methodology is focused on developing a method to recognize intricate handwritten mathematical equations. For pre-processing, Gray conversion and Weiner filtering are used. Segmentation is performed using the morphological operations, which increase the efficiency of the subsequent image of equation. Finally Neural Network based template matching technique is used to recognize the image of handwritten mathematical equation.

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“…1. It illustrates the main steps needed for propagating the input features and bias of a training pattern k as shown in [step lines [4][5][6][7][8][9][10][11][12][13][14]. Line 16 calculates the error in current pattern k. For updating the weight, we have two stages, namely updating the weights hidden-output as in steps 18-21, and updating the weights in the input to hidden layer interconnections, as in steps 23-31.…”

Section: B the Implemented Mlp Algorithmmentioning

confidence: 99%

“…The handwriting recognition process faces certain challenges [4]- [7]. The biggest one is that the handwritten images" various dimensions have to be normalized and/or processed in order for them to fall within the system"s boundary specific requirements.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Digit Recognition based on Output-Independent Multi-Layer Perceptrons

Keshta¹

2017

ijacsa

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With handwritten digit recognition being an established and significant problem that is facing computer vision and pattern recognition, there has been a great deal of research work that has been undertaken in this area. It is not a trivial task because of the big variation that exists in the writing styles that have been found in the available data. Therefore both, the features and the classifier need to be efficient. The core contribution of this research is the development of a new classification technique that is based on the MLP, which can be identified in handwritten documents as the binary digits '0' and '1'. This technique maps the different sets of various input data onto the MLP output neurons. An experimental evaluation of the technique's performance is provided. This evaluation is based on the well-known 'Pen-Based Recognition of Handwritten Digits' dataset, which is comprised of a total of 250 handwriting samples that are taken from 44 writers. The results obtained are very promising for such an approach in accurate handwriting recognition.

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Recognizing images of handwritten digits using learning vector quantization artificial neural network

Cited by 6 publications

References 4 publications

Identification of Rice Variety Using Geometric Features and Neural Network

Identification of Rice Variety Using Geometric Features and Neural Network

Identification of Handwritten Complex Mathematical Equations

Handwritten Digit Recognition based on Output-Independent Multi-Layer Perceptrons

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