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Title: :  Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks
PaperId: :  12876
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 6 Issue 5 2020
DUI:    16.0415/IJARIIE-12876
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Mohammed Ilyas AhmedMalla Reddy Engineering College
Mohammed SamranMalla Reddy Engineering College
Mohammed Abdul ThousifMalla Reddy Engineering College
P V Ramana MurthyMalla Reddy Engineering College

Abstract

Computer Science and Engineering
Support Vector Classifier, Confusion Matrix, Accuracy Score, Classification Report, Data Analysis, Neural Networks.
Optical Character Recognition (OCR) is the method of classification of optical patterns contained in an exceedingly digital image. The character recognition is achieved through segmentation, feature extraction, and classification. one of all the foremost used tools for optical character recognition may well be a tesseract. During this project we try to create an alternate of tesseract which is our own model ready to} recognize different handwritten digits which don't seem to be printed but written manually and since tesseract is solely able to recognize printed digits, it'll be a valuable feature for several developers. We use the Support Vector Machine during this process. Support Vector Machine could also be a classification tool at home with recognizing handwritten digits. We also use a Neural network for Data analysis and classification of knowledge. In particular, handwritten digit recognition has been applied to acknowledge amounts written on checks for banks and zip codes on envelopes for postal services. The handwritten digit recognition system may be divided into four stages, Data acquisition, Pre-processing, Feature extraction and Classification. Support Vector Machines, within the machine learning theory, are used for classification and analysis. they've supervised learning models with associated learning algorithms that analyze data and recognize patterns. As per the requirement of the information into consideration, the Support Vector Machines are modeled to classify. Support Vector and naive Bayes, both don't require the maximum amount of computation power compared to deep learning which makes it possible for the developers to deploy the model natively instead of running it in a server container.

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IJARIIE Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, and P V Ramana Murthy. "Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks" International Journal Of Advance Research And Innovative Ideas In Education Volume 6 Issue 5 2020 Page 1421-1428
MLA Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, and P V Ramana Murthy. "Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education 6.5(2020) : 1421-1428.
APA Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, & P V Ramana Murthy. (2020). Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks. International Journal Of Advance Research And Innovative Ideas In Education, 6(5), 1421-1428.
Chicago Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, and P V Ramana Murthy. "Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education 6, no. 5 (2020) : 1421-1428.
Oxford Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, and P V Ramana Murthy. 'Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks', International Journal Of Advance Research And Innovative Ideas In Education, vol. 6, no. 5, 2020, p. 1421-1428. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Recognition_of_Handwritten_Digits_Using_Support_Vector_Machine_and_Neural_Networks_ijariie12876.pdf (Accessed : 04 December 2023).
Harvard Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, and P V Ramana Murthy. (2020) 'Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks', International Journal Of Advance Research And Innovative Ideas In Education, 6(5), pp. 1421-1428IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Recognition_of_Handwritten_Digits_Using_Support_Vector_Machine_and_Neural_Networks_ijariie12876.pdf (Accessed : 04 December 2023)
IEEE Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, and P V Ramana Murthy, "Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks," International Journal Of Advance Research And Innovative Ideas In Education, vol. 6, no. 5, pp. 1421-1428, Sep-Oct 2020. [Online]. Available: https://ijariie.com/AdminUploadPdf/Recognition_of_Handwritten_Digits_Using_Support_Vector_Machine_and_Neural_Networks_ijariie12876.pdf [Accessed : 04 December 2023].
Turabian Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, and P V Ramana Murthy. "Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 6 number 5 (04 December 2023).
Vancouver Mohammed Ilyas Ahmed, Mohammed Samran, Mohammed Abdul Thousif, and P V Ramana Murthy. Recognition of Handwritten Digits Using Support Vector Machine and Neural Networks. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2020 [Cited : 04 December 2023]; 6(5) : 1421-1428. Available from: https://ijariie.com/AdminUploadPdf/Recognition_of_Handwritten_Digits_Using_Support_Vector_Machine_and_Neural_Networks_ijariie12876.pdf
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