The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.
Michael A. Nielsen, "Neural Networks and Deep Learning," Determination Press. 2015 (http://neuralnetworksanddeeplearning.com/chap1.html).
Raja, R., Kumar, S., Rani, S., & Laxmi, K.R. (Eds.). (2020). Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing (1st ed.). CRC Press. https://doi.org/10.1201/9780429354526.
N. M. Nasrabadi, "Pattern recognition and machine learning," J. Electron. Imaging, vol. 16, no. 4, p. 049901, 2007.
Chandrakar R, Raja R, Miri R, Tandan S. Vehicle Detection on Sanctuaries Using Spatially Distributed Convolutional Neural Network. sms [Internet]. 30Nov.2020 [cited 7May2021];12(SUP 3):116-21.
Y. Bengio, "Learning deep architectures for AI," Found. Trends® Mach. Learn., vol. 2, no. 1, pp. 1–127, 2009.
R. Plamondon and S. N. Srihari, "Online and off-line handwriting recognition: a comprehensive survey," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 63–84, 2000.
R. Chandrakar, R. Raja, R. Miri, S R Tandan, K. Ramya Laxmi, Detection and Identification of Animals in Wild Life Sancturies using Convolutional Neural Network, ‘International Journal of Recent Technology and Engineering (IJRTE)’ that will publish at Volume-8 Issue-5, January 2020 in Regular Issue on 30 January 2020.
K. Marukawa, M. Koga, Y. Shima, and H. Fujisawa, "A High-Speed Word Matching Algorithm for Handwritten Chinese Character Recognition.," in MVA, 1990, pp. 445–450.
Sumati Pathak, Pragya Bhatt, Rohit Raja, Vaibhav Sharma, " Weka VS Rapid Miner: Models Comparison in Higher Education with these Two Tools of Data, SAMRIDDHI: A Journal of Physical Sciences, pp-85-88, 2020.
F. Lauer, C. Y. Suen, and G. Bloch, "A trainable feature extractor for handwritten digit recognition," Pattern Recognit., vol. 40, no. 6, pp. 1816–1824, 2007.
L. Bottou et al., "Comparison of classifier methods: a case study in handwritten digit recognition," in Pattern Recognition, 1994. Vol. 2-Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on, 1994, vol. 2, pp. 77–82.
Pallavi S., Ramya Laxmi K., Ramya N., Raja R. (2020) Study and Analysis of Modified Mean Shift Method and Kalman Filter for Moving Object Detection and Tracking. In: Raju K., Govardhan A., Rani B., Sridevi R., Murty M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics. Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_76.
L. S. Oliveira, R. Sabourin, F. Bortolozzi, and C. Y. Suen, "A methodology for feature selection using multiobjective genetic algorithms for handwritten digit string recognition," Int. J. Pattern Recognit. Artif. Intell., vol. 17, no. 06, pp. 903–929, 2003.
Tiwari L., Raja R., Sharma V., Miri R. (2020) Fuzzy Inference System for Efficient Lung Cancer Detection. In: Gupta M., Konar D., Bhattacharyya S., Biswas S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_4.
N. Arica and F. T. Yarman-Vural, "An overview of character recognition focused on off-line handwriting," IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 31, no. 2, pp. 216–233, 2001.
H. Murase, M. Shinya, T. Wakahara, and K. Odaka, "Segmentation and recognition of handwritten character string using linguistic information," Trans Inst. Electron. Inf. Commun. Eng., vol. 69, no. 9, pp. 1292–1301, 1986.
Shraddha Shukla and Rohit Raja (2016) Digital Image Fusion using Adaptive Neuro-Fuzzy Inference System, International Journal of New Technology and Research (IJNTR), Vol. 2, Iss. 5, pp. 101-104, ISSN: 2454-4116.
R. K. Patra, R. Raja, T. S. Sinha, Md R. Mahmood (2018), Image Registration and Rectification using Background Subtraction method for Information security to justify Cloning Mechanism using High-End Computing Techniques, 3rd International Conference on Computational Intelligence and Informatics (ICCII-2018), held during 28-29 Dec 2018.
Talwar and Y. Kumar, "Machine Learning: An artificial intelligence methodology," Int. J. Eng. Comput. Sci., vol. 2, no. 12, 2013.
D. C. Cireşan, U. Meier, L. M. Gambardella, and J. Schmidhuber, "Deep, big, simple neural nets for handwritten digit recognition," Neural Comput., vol. 22, no. 12, pp. 3207–3220, 2010.
R K Patra, R Raja and T S Sinha, Extraction of geometric and prosodic features from human-gait-speech data for behavioural pattern detection: Part II, First International Conference on Advanced Computational and Communication Paradigms (ICACCP) will be held at Sikkim Manipal Institute of Technology (SMIT), Majitar, Rangpo, East Sikkim, Sikkim-737136 during 08-10 September 2017. ICACCP-2017.
N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst. Man Cybern., vol. 9, no. 1, pp. 62–66, 1979.
D. James et al., "A historical survey of algorithms and hardware architectures for neural inspired and neuromorphic computing applications," Biol. Inspired Cogn. Archit., vol. 19, pp. 49–64, Jan. 2017.
L. Tiwari, R. Raja, V. Awasthi, R. Miri “Detection of Nodule and Lung Segmentation Using Local Gabor XOR Pattern in CT Images”, Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing, CRC Press eBook ISBN9780429354526.
R Raja, S Kumar, S Rani, K. R Laxmi, “Lung Segmentation and Nodule Detection in 3D Medical Images Using Convolution Neural Network”, Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing, CRC Press eBook ISBN9780429354526.
M. I. Fanany, "Handwriting recognition on form document using convolutional neural network and support vector machines (CNN-SVM)," in Information and Communication Technology (ICoIC7), 2017 5th International Conference on, 2017, pp. 1–6.
S. R. Gunn, "Support vector machines for classification and regression," ISIS Tech. Rep., vol. 14, no. 1, pp. 5–16, 1998.
R. Beale and T. Jackson, Neural Computing-an introduction. CRC Press, 1990.
X.-X. Niu and C. Y. Suen, "A novel hybrid CNN–SVM classifier for recognizing handwritten digits," Pattern Recognit., vol. 45, no. 4, pp. 1318–1325, 2012.
Raja, R., Sinha, T.S., Dubey, R.P.: Orientation calculation of human face using symbolic techniques and ANFIS. Publ. Int. J. Eng. Futur. Technol. 7(7), 37–50. ISSN: 2455-6432 (2016).
Raja, R., Sinha, T.S., Dubey, R.P.: Recognition of human-face from side-view using progressive switching pattern and soft-computing technique. Assoc. Adv. Model. Simul. Tech. Enterp. Adv. B, 58(1), 14–34. ISSN:1240-4543 (2015).
Kumar S., Raja R., Gandham A. (2020) Tracking an Object Using Traditional MS (Mean Shift) and CBWH MS (Mean Shift) Algorithm with Kalman Filter. In: Johri P., Verma J., Paul S. (eds) Applications of Machine Learning. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3357-0_4.
T. S. Sinha, R. k. Patra, and R. Raja (2011) A Comprehensive analysis of human gait for abnormal foot recognition using Neuro-Genetic approach, International Journal of Tomography and Statistics (IJTS), Vol. 16, No. W11, pp. 56-73, ISSN: 2319-3339, http://ceser.res.in/ceserp/index.php/ijts.
K. Bansal and R. G. Kumar, "Cleaning and Recognition of Numerals in a Hand-written Devnagari Document Consisting of Roll Numbers and Marks," Ph.D. Thesis, 2012.
Dewangan K.P., Bonde P., Raja R. (2020) Application of Group Mobility Model for Ad Hoc Network. In: Pati B., Panigrahi C., Buyya R., Li KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1089. Springer, Singapore. https://doi.org/10.1007/978-981-15-1483-8_31.
S. Pathak, R. Raja, V. Sharma, and K. Ramya Laxmi, A Framework of ICT Implementation on Higher Educational Institution with Data Mining Approach, European Journal of Engineering Research and Science, ISSN (Online): 2506-8016.
F. Nie, C. Gao, Y. Guo, and M. Gan, "Two-dimensional minimum local cross-entropy thresholding based on co-occurrence matrix," Comput. Electr. Eng., vol. 37, no. 5, pp. 757–767, 2011.
N. Rawat, R. Raja (2016), Moving Vehicle Detection and Tracking using Modified Mean Shift Method and Kalman Filter and Research, International Journal of New Technology and Research (IJNTR), Vol. 2, Iss. 5, pp. 96-100, ISSN: 2454-4116.
L. Tiwari, R. Raja, V. Awasthi, R. Miri, G.R. Sinha, Monagi H. Alkinani, K. Polat, Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms, Measurement, Volume 172, 2021, 108882, ISSN 0263-2241.
H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max dependency, max-relevance, and min-redundancy," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226–1238, 2005.
S K Vishwakarma, P C Sharma, R Raja, V Roy, S Tomar, An Effective Cascaded Approach for EEG Artifacts Elimination, International Journal of Pharmaceutical Research, Vol 12, Issue 4, ISSN 0975-2366, https://doi.org/10.31838/ijpr/2020.12.04.653 2.
B. Scholkopf et al., "Comparing support vector machines with Gaussian kernels to radial basis function classifiers," IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2758–2765, 1997.
R. Raja, R. k. Patra, T. S. Sinha (2017), Extraction of Features from Dummy face for improving Biometrical Authentication of Human, International Journal of Luminescence and Application, ISSN:1 2277-6362, Vol. 7, No. 3-4, Oct Dec 2017, Article 259, pp. 507-512.
Bagdanov and J. Kanai, "Projection profile based skew estimation algorithm for JBIG compressed images," in Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference, 1997, vol. 1, pp. 401–405.
C.-L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, "Hand-written digit recognition: investigation of normalization and feature extraction techniques," Pattern Recognit., vol. 37, no. 2, pp. 265–279, 2004.
S. Kahan, T. Pavlidis, and H. S. Baird, "On the recognition of printed characters of any font and size," IEEE Trans. Pattern Anal. Mach. Intell., no. 2, pp. 274–288, 1987.
L. Tiwari, R. Raja, V. Sharma, R. Miri, Adaptive Neuro Fuzzy Inference System Based Fusion of Medical Image, International Journal of Research in Electronics and Computer Engineering, Vol 7, Iss. 2, pp. 2086-2091, ISSN: 2393-9028 (PRINT) |ISSN: 2348-2281.
F. C. Ribas, L. S. Oliveira, A. S. Britto, and R. Sabourin, "Hand-written digit segmentation: a comparative study," Int. J. Doc. Anal. Recognit. IJDAR, vol. 16, no. 2, pp. 127–137, 2013.
R. Raja, S. Kumar, Md Rashid, Color Object Detection Based Image Retrieval using ROI Segmentation with Multi-Feature Method, in Wireless Personal Communication Springer Journal, Print ISSN0929-6212 online ISSN1572-834 pp-1-24, https://doi.org/10.1 007/s11277-019-07021-6.
Y.-K. Chen and J.-F. Wang, "Segmentation of single-or multiple-touching handwritten numeral string using background and foreground analysis," IEEE Trans. Pattern Anal. Mach. Intell., no. 11, pp. 1304–1317, 2000.
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