Pen Stroke Digit Recognition Using CNN
DOI:
https://doi.org/10.32628/CSEIT24102103Keywords:
CNN, KNN, OCR, Naïve Bayes, Neural Networks, Feature Extraction, Optical Character Recognition, Segmentation, Handwritten Digit ClassificationAbstract
Hand-written character and digit recognition have been one of the most exigent and engrossing field of pattern recognition and image processing. The main aim of this paper is to demonstrate and represent the work which is related to hand-written digit recognition. The hand-written digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of hand-written numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. KNN is applied for recognition and classification. The digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. CNN is applied for recognition, classification and we are also getting nice accuracy while doing prediction.
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Swapna Prava Ekka, “Recognition of Handwritten Digits using Proximal Support Vector Machine”, (2014), National Institute of Technology Rourkela, unpublished.
Viragkumar N. Jagtap, Shailendra K. Mishra, “Fast Efficient Artificial Neural Network for Handwritten Digit Recognition”, (IJCSIT) International Journal of Computer Science and Information Technologies,Vol. 5 (2), (2014), ISSN Number: 0975-9646, pp. 2302-2306.
Saeed AL-Mansoori, “Intelligent Handwritten Digit Recognition using Artificial Neural Network”, Journal of Engineering Research and Applications, Vol. 5, Issue 5, (Part -3) May (2015), ISSN : 2248- 9622, pp.46-51.
S M Shamim, Mohammad Badrul Alam Miah, Angona Sarker, Masud Rana, Abdullah Al Jobair, “Handwritten Digit Recognition using Machine Learning Algorithms”, Journal of Computer Science and Technology: DNeural & Artificial Intelligence, vol. 18, Issue 1, (2018), ISSN: 0975-4172, pp.0975-4350. DOI: https://doi.org/10.17509/ijost.v3i1.10795
Md. Anwar Hossain, Md. Mohon Ali, “Recognition of Handwritten Digit using Convolutional Neural Network (CNN)”, Journal of Computer Science and Technology: D Neural & Artificial Intelligence, Vol.19, Issue 2, (2019), ISSN: 0975-4172, pp.0975-4350.
Saqib Ali, Zeeshan Shaukat, Muhammad Azeem, Zareen Sakhawat, Tariq Mahmood, Khalilur Rehman, “An ecient and improved scheme for hand written digit recognition based on convolutional neural network”, (2019), unpublished. DOI: https://doi.org/10.1007/s42452-019-1161-5
Savita Ahlawat, Amit Choudhary, Anand Nayyar, Saurabh Singh, Byungun Yoon, “Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)”, (2020), unpublished. DOI: https://doi.org/10.3390/s20123344
Aarti Gupta, Rohit Miri, Hiral Raja, “Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques”, Journal of Engineering and Technology Research, (2021), ISSN: 2736-576X.
V. Gopalakrishan, R. Arun, L. Sasikumar, K. Abhirami, “Handwritten Digit Recognition for Banking System”, Kings College of Engineering, Punalkulam, Pudukottai, Journal of Engineering Research & Technology (IJERT), (2021), ISSN: 2278-0181.
Ritik Dixit, Rishika Kushwah, Samay Pashine, “Handwritten Digit Recognition using Machine and Deep Learning Algorithms”, Journal of Computer Applications, Vol. 176 – No. 42, July (2020), pp.0975 – 8887. DOI: https://doi.org/10.5120/ijca2020920550
Kiran Banjare, Sampada Massey, “Handwritten Numeric Digit Classification and Recognition: Recent Advancements”, Journal of Emerging Technologies in Engineering Research (IJETER), Vol. 4, Issue 6, June (2016), ISSN: 2454-6410.
Pranit Patil, Bhupinder Kaur, “Handwritten Digit Recognition Using Various Machine Learning Algorithms and Models”, Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol. 8, Issue- 4, July (2020), ISSN: 2347-5552. DOI: https://doi.org/10.21276/ijircst.2020.8.4.16
Bushra Aurangzeb, Memuna Malik, “A Survey on Handwritten Digit Recognition”, COMSATS Institute of Information technology , Vol. 4, Issue 2, April (2015), ISSN: 2306-708X.
Narender Kumar, Himanshu Beniwal, “Survey on Handwritten Digit Recognition using Machine Learning”, H.N.B. Garhwal University, Vol-6, Special Issue-5, June (2018), ISSN: 2347-2693.
Akanksha Gupta, Ravindra Pratap Narwaria, Madhav Singh, “Review on Deep Learning Handwritten Digit Recognition using Convolutional Neural Network”, Journal of Recent Technology and Engineering (IJRTE), Vol. 9 Issue-5, January (2021), ISSN: 2277-3878. DOI: https://doi.org/10.35940/ijrte.E5287.019521
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