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Relevant bibliographies by topics / Handwriting digit recognition

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Academic literature on the topic 'Handwriting digit recognition'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Handwriting digit recognition"

1

Alugunuri, Sai Sharan, Kaithapuram Vishal Reddy, Chevvula Shiva Kumar, and T. Bhavani Prasad. "Handwritten Digit Prediction Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 2040–43. http://dx.doi.org/10.22214/ijraset.2023.49884.

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Abstract: For many years, numerous methods have been used in extensive research on handwriting recognition. The capacity to create an effective algorithm that can recognise handwritten digits given by users via scanner, tablet, and other digital devices is at the core of the issue. The automatic processing of bank checks, postal addresses, and other sorts of data already makes substantial use of handwritten digit recognition. Computational intelligence methods like artificial neural networks used by several current systems. CNN and the MNIST data set will be used to complete this. Handwriting

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Ahlawat, Savita, and Rahul Rishi. "A Genetic Algorithm Based Feature Selection for Handwritten Digit Recognition." Recent Patents on Computer Science 12, no. 4 (2019): 304–16. http://dx.doi.org/10.2174/2213275911666181120111342.

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Background: The data proliferation has been resulted in large-scale, high dimensional data and brings new challenges for feature selection in handwriting recognition problems. The practical challenges like the large variability and ambiguities present in the individual’s handwriting style demand an optimal feature selection algorithm that would be capable to enhance the recognition accuracy of handwriting recognition system with reduced training efforts and computational cost. Objective: This paper gives emphasis on the feature selection process and proposed a genetic algorithm based feature s

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Ramya, Atchuta, B. Buddana V. S. S. Sri Rama Varshith, Allada Nomiya, Deva V. Naga Sai Siva Ganesh, and Balla Dinesh Babu. "DIGITDETECT: A CNN-BASED SYSTEM FOR MANUAL HANDWRITING RECOGNITION." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 91–95. https://doi.org/10.36893/jnao.2025.v16i01.011.

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The project "DigitDetect: A Handwritten Digit Recognition Using CNN" focuses on developing an advanced system for recognizing handwritten digits with high accuracy. Leveraging Convolutional Neural Networks (CNNs), the system overcomes the challenges faced by traditional methods like KNearest Neighbors (KNN), such as variations in handwriting styles, sizes, and orientations. By using the MNIST dataset comprising 28x28 grayscale images, the model undergoes data preprocessing and augmentation, ensuring robust generalization. This approach highlights the efficiency of CNNs in automatically extract

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Mhaske, Anirudh, Atharv Joshi, Dattaram Kajrekar, Ruturaj Jugdar, and Prof Ajita Mahapadi. "Digit Recognition Using MNIST Dataset." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1862–65. http://dx.doi.org/10.22214/ijraset.2022.46930.

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Abstract: In this paper, we have performed handwritten digit recognition using MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Convolution Neural Network (CNN) models. Our main goal is to compare the accuracy of the above models along with their execution time to obtain the best possible model for digit recognition. Reliability of humans over machines has never been so high that from classifying objects in photographs to adding sound to silent movies can all be done using deep learning and machine learning algorithms. Similarly, handwriting recognition is o

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Patel, Tarun, Shivansh Tiwari, Vaibhav Dubey, Vaibhav Singh, and Harvendra Kumar. "Feature Selection for Handwriting Digit Recognition Using Convolutional Neural Network." International Journal of Science and Research (IJSR) 11, no. 5 (2022): 1480–85. http://dx.doi.org/10.21275/sr22515114611.

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Dey, Asst Prof Moumita. "ScriptCalc: Handwriting Recognition with Mathematical Equation Solving." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 2413–24. http://dx.doi.org/10.22214/ijraset.2024.61902.

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Abstract: The present paper proposes a novel algorithm for recognition of handwritten digits. For this, the present paper classified the digits into two groups: one group consists of blobs with/without stems and the other digits with stems only. The blobs are identified based on a new concept called morphological region filling methods. This eliminates the problem of finding the size of blobs and their structuring elements. The digits with blobs and stems are identified by a new concept called ‘connected component’. This method completely eliminates the complex process of recognition of horizo

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Tuba, Ira, Una Tuba, and Mladen Veinović. "Classification methods for handwritten digit recognition: A survey." Vojnotehnicki glasnik 71, no. 1 (2023): 113–35. http://dx.doi.org/10.5937/vojtehg71-36914.

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Introduction/purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset. Methods: The paper analyzes, synthesizes and compares the development of different classifiers applied to the handwritten digit recognition problem, from linear classifiers to convolutional neural networks. Results: Handwritten digit recognition classification accuracy tested on the MNIST dataset while using training and testing sets is now higher than 99.5% and the most successful method is a convolutional neural network. Conclusions: Handwritten digit recognition is a prob

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Ahlawat, Savita, Amit Choudhary, Anand Nayyar, Saurabh Singh, and Byungun Yoon. "Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)." Sensors 20, no. 12 (2020): 3344. http://dx.doi.org/10.3390/s20123344.

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Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks

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Zemlock, Deborah Y. "Learning About Letters Through Handwriting Practice." IU Journal of Undergraduate Research 2, no. 1 (2016): 56–62. http://dx.doi.org/10.14434/iujur.v2i1.20924.

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The influence of visual-motor experiences with written symbols on pre-reading abilities, such as letter knowledge, have been shown to be facilitatory in both correlational studies on very young children and in experimental studies on older children. However, it is not known whether any fine-motor practice will create this benefit, whether it is specific to writing letters, or whether certain ages would benefit most from handwriting practice. Here, we hypothesized that immature fine-motor skill that produces variable forms may be crucial to the beneficial effects of handwriting training – predi

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Tamanna Sachdeva, Et al. "A Novel Approach for Hand-written Digit Classification Using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1627–35. http://dx.doi.org/10.17762/ijritcc.v11i9.9148.

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Humans' control over technology is at an all-time high, with applications ranging from visual object recognition to the dubbing of dialogue into silent films. Using algorithms for deep learning and machine learning. Similarly, the most crucial technologies are text line recognition fields of study and development, with an increasing number of potential outcomes. Handwriting recognition (HWR), also identified as Handwriting Text Acknowledgment, is the capacity of a computer to understand legibly handwritten input from bases such as paper documents, screens, and other devices. Evidently, we have

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Book chapters on the topic "Handwriting digit recognition"

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Price, David, and Stefan Knerr. "Cooperation of Feedforward Neural Networks for Handwritten Digit Recognition." In Fundamentals in Handwriting Recognition. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78646-4_21.

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Hasan, Fuad, Shifat Nayme Shuvo, Sheikh Abujar, Md Mohibullah, and Syed Akhter Hossain. "Bangla Continuous Handwriting Character and Digit Recognition Using CNN." In Innovations in Computer Science and Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2043-3_60.

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Zhang, YingChao, TaiLei Liu, and XiaoLing Ye. "Handwriting Digit Recognition Based on Fractal Edge Feature and BP Neural Net." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30126-1_75.

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Motoki, Minoru, Heitaro Hirooka, Youta Murakami, Ryuji Waseda, and Terumitsu Nishimuta. "An Evaluation of Handwriting Digit Recognition Using Multilayer SAM Spiking Neural Network." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47508-5_8.

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Liu, Zhi-Qiang, Jinhai Cai, and Richard Buse. "Markov Random Field Model for Recognizing Handwritten Digits." In Handwriting Recognition. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44850-1_5.

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Liu, Zhi-Qiang, Jinhai Cai, and Richard Buse. "Hidden Markov Model-Based Method for Recognizing Handwritten Digits." In Handwriting Recognition. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44850-1_3.

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Kaensar, Chayaporn. "A Comparative Study on Handwriting Digit Recognition Classifier Using Neural Network, Support Vector Machine and K-Nearest Neighbor." In The 9th International Conference on Computing and InformationTechnology (IC2IT2013). Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37371-8_19.

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Niu, Kai, Fusang Zhang, Xiaolai Fu, and Beihong Jin. "In-Air Handwriting Recognition Using Acoustic Impulse Signals." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_25.

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AbstractThis paper presents AcousticPAD, a contactless and robust handwriting recognition system that extends the input and interactions beyond the touchscreen using acoustic signals, thus very useful under the impact of the COVID-19 epidemic. To achieve this, we carefully exploit acoustic pulse signals with high accuracy of time of fight (ToF) measurements. Then we employ trilateration localization method to capture the trajectory of handwriting in air. After that, we incorporate a data augmentation module to enhance the handwriting recognition performance. Finally, we customize a back propag

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Liu, Chang, Rize Jin, Liang Wu, Ziyang Liu, and Mingming Guo. "An Improved AdaBoost Algorithm for Handwriting Digits Recognition." In Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3863-6_25.

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Mahmud, Tanjim, Taohidur Rahman, Mohammad Tarek Aziz, et al. "Handwriting Recognition of English Digits: A Deep Learning Perspective." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-73324-6_10.

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Conference papers on the topic "Handwriting digit recognition"

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Huimin Xiao and Chen Liu. "Handwriting digit recognition based on extension engineering." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5192803.

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Codrescu, Cristinel. "FIRMLP for Handwritten Digit Recognition." In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2016. http://dx.doi.org/10.1109/icfhr.2016.0095.

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Shiddieqy, Hasbi Ash, Trio Adiono, and Infall Syafalni. "Mobile Client-Server Approach for Handwriting Digit Recognition." In 2019 International Symposium on Electronics and Smart Devices (ISESD). IEEE, 2019. http://dx.doi.org/10.1109/isesd.2019.8909448.

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Chouaib, Hassan, Florence Cloppet, and Nicole Vincent. "Fast Feature Selection for Handwritten Digit Recognition." In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2012. http://dx.doi.org/10.1109/icfhr.2012.203.

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Cirstea, Bogdan-Ionut, and Laurence Likforman-Sulem. "Tied Spatial Transformer Networks for Digit Recognition." In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2016. http://dx.doi.org/10.1109/icfhr.2016.0102.

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Dehghanian, Atefeh, and Vahid Ghods. "Farsi Handwriting Digit Recognition Based on Convolutional Neural Networks." In 2018 6th International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2018. http://dx.doi.org/10.1109/iscbi.2018.00022.

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Yogesh, Yogesh, G. S. Pradeep Ghantasala, and Annu Priya. "Artificial Intelligence Based Handwriting Digit Recognition (HDR) - A Technical Review." In 2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT). IEEE, 2023. http://dx.doi.org/10.1109/dicct56244.2023.10110186.

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Abu Ghosh, Mahmoud M., and Ashraf Y. Maghari. "A Comparative Study on Handwriting Digit Recognition Using Neural Networks." In 2017 International Conference on Promising Electronic Technologies (ICPET). IEEE, 2017. http://dx.doi.org/10.1109/icpet.2017.20.

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Abbas, Nassim, Youcef Chibani, and Hassiba Nemmour. "Handwritten Digit Recognition Based on a DSmT-SVM Parallel Combination." In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2012. http://dx.doi.org/10.1109/icfhr.2012.208.

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Impedovo, S., G. Pirlo, and F. M. Mangini. "Handwritten Digit Recognition by Multi-objective Optimization of Zoning Methods." In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2012. http://dx.doi.org/10.1109/icfhr.2012.209.

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