Vijay Mane, Ruta Sapate, Samruddhi Raut et al. · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: This paper presents a powerful Handwritten Digit Recognition System that combines a Graphical User Interface (GUI) based on Tkinter with Convolutional Neural Networks (CNNs). Our approach, which makes use of the MNIST dataset, includes careful data pretreatment to facilitate efficient CNN model training. Convolutional and pooling layers are included in the design of the model, and they are optimized with the Adam optimizer for higher learning rates. The evaluation's findings demonstrate excellent memory, accuracy, and precision. Furthermore, real-time digit drawing is made possible via an intuitive Tkinter GUI, which confirms the model's applicability. By showing the effectiveness of CNNs and offering an interactive platform for natural user interaction, the research provides a holistic solution to handwritten digit recognition. This method is promising for various uses in digit recognition scenarios, highlighting its flexibility and usefulness in real-world situations.
Cited by 3Open access
G. Oviya, N. K. Sakthivel · International Journal of Innovative Research in Engineering · 2024
Handwritten digit recognition is the intelligence of computers to recognize digits written by humans. But it becomes one of the most challenging tasks for machines as handwritten digits are not perfect and can be made with many different: flavors, size, thickness. Thus, as a solution to this problem, Handwriting digit recognition model comes into picture. Many machine learning techniques have been employed to solve the handwritten digit recognition problem. This paper focuses on Neural Network (NN) approaches. Among the three famous NN approaches: deep neural network (DNN), deep belief network (DBN) and convolutional neural network (CNN), the specialization of CNN as compared to other NN of being able to detect pattern is what makes it so useful for recognizing handwritten digits. Humans can very easily see, read & write any handwritten digits, when written in proper format. Even if the digits are not written in proper format we can use our logic and predict what digit it could be. But It is a hard task for the machine to recognize handwritten digits as these are not perfect and can be made with many different flavors. Thus handwritten digit recognition is the solution to this problem which uses the image of a digit and recognizes the digit present in the image. The handwritten digit recognition is the ability of computers to recognize human handwritten digits. The goal is to build a model that can efficiently and reliably recognize the digits and output the proper result. Amongst all the other neural networks, working and implementing a model using Convolution Neural Network gives out the most precise results. It is most popularly used for analyzing images as well as for other data analysis or classification problems. CNN has hidden layers called convolutional layers. These layers work the same way as other layers do but here we need to specify the no of filters each layer should have These filters are actually what detects the pattern. Patterns could be edges, corners, circles or any complex other objects like eyes, ears or even deeper full dogs, cats, etc. Thus, the specialization of CNN as compared to other NN of being able to detect patterns is what makes it so useful for recognizing handwritten digits Key Word: Neural Networks; Convolutional Neural Networks (CNN); Image Processing; Optical Character Recognition (OCR); MNIST Dataset
Cited by 3
Jyoti Shinde, Chaitali R. Rajput, Prof. Mrunal Shidore Prof. Milind Rane · International Journal of Trend in Scientific Research and Development · 2018
The main objective of this paper is to recognize and predict handwritten digits from 0 to 9 where data set of 5000 examples of MNIST was given as input. As we know as every person has different style of writing digits humans can recognize easily but for co it is comparatively a difficult task so here we have used neural network approach where in the machine will learn on itself by gaining experiences and the accuracy will increase based upon the experience it gains. The dataset was trained using feed neural network algorithm. The overall system accuracy obtained was 95.7%
Cited by 14Open access
Kantar, Martin · Brno University of Technology Digital Library (Brno University of Technology) · 2012
The aim of this diploma thesis is to explain what speech is and what are its constituents. I mention commonly used methods which are used for preparation of signals which we use for recognition. Schematic examples show principles of current recognizers of speech, their advantages and disadvantages. I made speech recognition program for 0-9 numerals in Matlab for neural nets learning.
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Jyoti Shinde ; Chaitali Rajput ; Prof. Mrunal Shidore ; Prof. Milind Rane · Bulletin of Miscellaneous Information (Royal Gardens Kew)
The main objective of this paper is to recognize and predict handwritten digits from 0 to 9 where data set of 5000 examples of MNIST was given as input. As we know as every person has different style of writing digits humans can recognize easily but for computers it is comparatively a difficult task so here we have used neural network approach where in the machine will learn on itself by gaining experiences and the accuracy will increase based upon the experience it gains. The dataset was trained using feed forward neural network algorithm. The overall system accuracy obtained was 95.7% Jyoti Shinde | Chaitali Rajput | Prof. Mrunal Shidore | Prof. Milind Rane"Handwritten Digit Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8384.pdf Article URL: http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/8384/handwritten-digit-recognition/jyoti-shinde
Cited by 0
Swarup Kumar Supakar, Mubasshir Ali, Das, Sutapa et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
<em>Project deals with the applications of ML (Machine Learning ) techniques for detecting Hand written digit classification, with many real-world applications such as digitizing historical documents, recognizing handwritten addresses on envelopes, and processing handwritten forms. In this project, we aimed to develop a machine learning model that can accurately identify and classify handwritten digits from an image. We trained our model on a dataset of handwritten digit images, the MNIST dataset, using convolutional neural network (CNN) architecture. Our preprocessing techniques included resizing, normalization, and augmentation. We evaluated our model on a separate set of test images and achieved an accuracy of 99.3 Our results demonstrate the effectiveness of CNN architecture for handwritten digit recognition, as well as the importance of preprocessing techniques in improving accuracy. We discuss potential areas for further research, such as exploring different CNN architectures or datasets, and the implications of our findings for real world applications. Overall, this project serves as an example of the potential of machine learning and computer vision to automate tasks and improve efficiency.</em>
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Hameem Khan P, Mr. Akshay Nayak B.S, R S Abhijna et al. · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2025
Abstract—Handwritten digit recognition has become a key area of research in the fields of computer vision,machine learning and pattern recognition. This project focuses on development of robust model for handwritten English digit recognition imple- menting CNN, a class of the deep learning algorithms particularly well-suited for image pro- cessing tasks. The model is trained and judged on the MNIST dataset, which consists of grayscale images of handwritten digits from 0 to 9. The CNN architecture employed in this project incorporates convolutional layers for the feature extraction, pooling layers for the dimension reduction and connected layers for the classification. Through extensive experimentation and hyperparameter tuning, the model achieves a high accuracy in digit recognition, demonstrating the effectiveness of CNNs in learning spatial hierarchies of features from input images. This system can be applied in areas such as postal automation, bank check processing, and digitization of handwritten forms. Index Terms—Handwritten Digit Recognition, Random Forest, SVM, Graph Neural Networks, Chain Codes, MNIST,Feature extraction, Machine Learning, CNN
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Partha Chakraborty, Syeda Surma Jahanapi, Tanupriya Choudhury · Lecture notes in networks and systems · 2021
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Davor Zink, Daniel N. Allen · 2018
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Hrudai Pavankumar S P, Akshay Nayak B.S, Abhijna R S et al. · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2025
Abstract—Understanding handwritten digits through auto- mated systems is a fundamental requirement in many intelligent applications. This work introduces a deep learning–based solution for recognizing handwritten English digits using the Convolutional Network (CNN) architecture. The model which we are using is trained and validated using the MNIST dataset, which consists of grayscale digit images covering numbers from zero through nine. In contrast to traditional approaches that rely on manually crafted features, the system which we have proposed learns meaningful representations directly from image pixels. Experimental results describe consistent accuracy of results across diverse handwriting styles. The developed model can be effectively applied in automated document processing, form digitization, and financial data entry, while also providing a scalable base for future handwritten text recognition systems. Index Terms—Handwritten Digit Recognition, Convolutional Neural Network, MNIST Dataset, Deep Learning, Image Classi- fication
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Jürgen Franke · WORLD SCIENTIFIC eBooks · 1997
Cited by 16
Markus Diem, Stefan Fiel, Angelika Garz et al. · Zenodo (CERN European Organization for Nuclear Research) · 2018
The CVL Single Digit dataset consists of 7000 single digits (700 digits per class) written by approximately 60 different writers. The validation set has the same size but different writers. The validation set may be used for parameter estimation and validation but not for supervised training. The CVL Digit Strings dataset uses 10 different digit strings from a total of about 120 writers resulting in 1262 training images. The digits from the CVL Single Digit dataset were extracted from these strings. This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to: Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, <em>ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013)</em>, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013.
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Markus Diem, Stefan Fiel, Angelika Garz et al. · Zenodo (CERN European Organization for Nuclear Research) · 2018
The CVL Single Digit dataset consists of 7000 single digits (700 digits per class) written by approximately 60 different writers. The validation set has the same size but different writers. The validation set may be used for parameter estimation and validation but not for supervised training. The CVL Digit Strings dataset uses 10 different digit strings from a total of about 120 writers resulting in 1262 training images. The digits from the CVL Single Digit dataset were extracted from these strings. This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to: Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, <em>ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013)</em>, In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013.
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S Nithyapriya, C Shanthini, S Suvalaxmi et al. · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2023
This project explores the application of Convolutional Neural Networks in Handwritten digit recognition. Exploiting the widely used MNIST dataset, we proposed a deep learning model that encompasses the Ensemble model that is capable of predicting and recognizing the handwritten digits and achieves remarkable precision. The dataset contains 70000 images in total, where 55000 images are for training, 5000 images for validating, and 10000 images for testing. By using CNN, we surpassed the traditional way of training the algorithm with machine learning. By incorporating robust pre-processing techniques and innovative training ideas and strategies, our model showcases resilience to real-world problems and challenges which includes reading the postal codes in the written mail, reading the digits in the handwritten checks, digits drawn on the mobile touch panel, etc. To better extract the features of the complex handwritten digits, bagging is used from the Ensemble techniques. The objective of this project is to find a better optimizer to work with ensemble models, which encompasses the merging of optimizers with the deep learning models. Key Words: Handwritten digits, CNN, Ensemble Model, Optimizers, MNIST.
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Ankit Kumar, Aashima Aashima, Akshay Saini et al. · SSRN Electronic Journal · 2024
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Piyush Mishra · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: This research paper provides a comprehensive analysis of the application of Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for the task of handwritten digit recognition, focusing on the extensively studied MNIST dataset. The study delves into the strengths and weaknesses of both approaches, considering various aspects such as accuracy, computational efficiency, and robustness. Through an in-depth exploration of the literature and empirical evidence, this paper aims to offer valuable insights into the advancements, challenges, and future directions in the field of handwritten digit recognition.
Cited by 1Open access
Ekene F. Ozioko, Chioma Uchechukwu Ugwa, Izuchukwu Louis Akwudi · International journal of research and scientific innovation · 2025
The application of neural networks to digital recognition through a relatively easy-to-understand by the general public cannot be over emphasize. This paper investigated the several techniques used for preprocessing the handwritten digits, as well as several ways in which neural networks are used for the digital recognition task. Whereas the main goal was a purely educational one, a moderate recognition rate of 98% was reached on a test set.
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M. Narasimha Murty, V. Susheela Devi · Undergraduate topics in computer science · 2011
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2008
Abstract — Recent results in pattern recognition have shown that SVM (Support Vector Machine) classifiers often have superior recognition rates in comparison to other classification methods. In this paper, a cooperation of four SVM classifiers for handwritten digit recognition, each using different feature set is examined. We investigate the advantages and weaknesses of various cooperation schemes based on classifier decision fusion using statistical reasoning. The obtained results show that it is difficult to exceed the recognition rate of a single, well-tuned SVM classifier applied straightforwardly on all feature sets. In our experiments only one of the cooperation schemes exceeds the recognition rate of a single SVM classifier. However, the classifier cooperation reduces the classifier complexity and need for training samples, decreases classifier training time and sometimes improves the classifier performance.
Cited by 0
Anca Ignat, Bogdan Aciobanitei · 2016
Handwritten digit recognition is a subproblem of the well-known optical recognition topic. In this work, we propose a new feature extraction method for offline handwritten digit recognition. The method combines basic image processing techniques such as rotations and edge filtering in order to extract digit characteristics. As classifiers, we use k-NN (k Nearest Neighbor) and Support Vector Machines (SVM). The methods are tested on a commonly employed database of handwritten digits' images, MNIST (Mixed National Institute of Standards and Technology) on which the classification rate is over 99%.
Cited by 11