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Journal articles on the topic 'Handwriting digit recognition'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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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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Tutar, Mehmet. "Comparison of Handwritten Recognition Methods on Arabic and Latin Characters." Journal of Studies in Science and Engineering 2, no. 3 (2022): 22–30. http://dx.doi.org/10.53898/josse2022232.

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In this article, both machine learning techniques and deep learning methods were applied on the digit datasets created using the Arabic and Latin alphabets, and the performances of the methods were compared. Each method was tested with various parameters and the results were analyzed. In addition, with this study, the recognizability of handwritten numeral datasets created using different alphabets was also observed. For experiments, an Arabic alphabet handwritten digit dataset (60,000 training and 10,000 testings) and a Latin alphabet handwritten digit dataset (60,000 training and 10,000 test

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Rastogi, Rohit, Himanshu Upadhyay, Akshit Rajan Rastogi, et al. "Knowledge Extraction in Digit Recognition Using MNIST Dataset." International Journal of Knowledge Management 17, no. 4 (2021): 52–75. http://dx.doi.org/10.4018/ijkm.2021100103.

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In handwriting recognition, traditional systems have relied heavily on handcrafted features and a massive amount of prior data and knowledge. Deep learning techniques have been the focus of research in the field of handwriting digit recognition and have achieved breakthrough performance in the last few years for knowledge extraction and management. KM and knowledge pyramid helps the project with its relationship with big data and IoT. The layers were selected randomly by which the performance of all the cases was found different. Data layers of the knowledge pyramid are formed by the sensors a

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Li Cui, Li Cui, Ting-Xuan Chen Li Cui, Ying-Qing Xia Ting-Xuan Chen, Xia Cao Ying-Qing Xia, and Ling Wu Xia Cao. "Ensemble Learning Network for Handwritten Digit Recognition Based on Fusion Optimized CNN." 電腦學刊 34, no. 3 (2023): 137–50. http://dx.doi.org/10.53106/199115992023063403010.

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<p>Handwritten digit recognition is an active research field. These recognition systems are faced with many challenges, including accuracy, speed and automatic extraction of complex handwriting features. In this paper, a Stacking ensemble learning model based on fusion optimized CNN is proposed, which can be effectively used for handwritten digit recognition. To better extract the features of complex handwritten digital images and maximize the reliability of the model, the Bagging strategy combined with six CNNs is used for feature extraction for the first time, and SVM is used for class

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Sanjay A V, Ranjith R, and Dr P Kavipriya. "Digit Recognition Using CNN and MNIST Dataset." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1142–51. https://doi.org/10.47392/irjaeh.2025.0163.

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Handwritten Digit Recognition (HDR) techniques are gaining traction in both academic and industrial domains. The complexity of recognizing handwritten digits arises from the diverse and intricate patterns involved. Identifying words in low-resource scripts presents significant challenges and is often time-intensive. Improving the performance of deep learning (DL) models, especially neural networks, can be achieved by expanding training datasets and incorporating sample randomization. Traditional HDR methods typically depend on manually extracted key point features. Variations in handwriting st

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Chayti, Saha, Masuma Fozilatunnesa, Ahammad Khalil, Shahriar Muzammel Chowdhury, and Mohibullah Md. "Real time Bangla Digit Recognition through Hand Gestures on Air Using Deep Learning and OpenCV." International Journal of Current Science Research and Review 05, no. 02 (2022): 435–45. https://doi.org/10.5281/zenodo.6092684.

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Abstract : Digit Recognition in real time through hand gestures has achieved great attention in machine learning and computer vision applications. This article focuses on identifying Bangla numerals in the air using hand motions. This research leads to the stairwell, allowing for more investigation in the same subject for various Bangla characters and even phrases. The major issue, however, is coping with the wide range of handwriting styles employed by various users. Many studies have been done on the identification of Bangla handwritten digits, but none has proven successful at recognizing B

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A.Leela, Vathi, Rani V.Jyothsna, Siva Prasad A.Devi, Nikitha S.Sai, Lakshmi D.Vijaya, and K.Renuka. "An in-Depth Deep Learning Approach to Handwritten Digits Recognition." An in-Depth Deep Learning Approach to Handwritten Digits Recognition 8, no. 12 (2023): 7. https://doi.org/10.5281/zenodo.10405523.

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Due to the variations in human handwriting, computerized handwritten digit recognition is a challenging task. This abstract describes a system that identifies handwritten digits in images and documents using Convolutional Neural Networks built with PyTorch. In order to solve a variety of practical problems, this technology is crucial in applications like check processing, postal sorting, and number plate recognition. The abstract compares different machine learning and deep learning algorithms, such as Support Vector Machine, Multilayer Perceptron, and Convolutional Neural Network, based on th

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Impedovo, S., F. M. Mangini, and D. Barbuzzi. "A novel prototype generation technique for handwriting digit recognition." Pattern Recognition 47, no. 3 (2014): 1002–10. http://dx.doi.org/10.1016/j.patcog.2013.04.016.

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VINITA, PATIL. "Modeling of Hybrid CNN Framework for Handwritten Character and Digit Recognition System." Advanced Engineering Science 54, no. 2 (2023): 661–73. https://doi.org/10.5281/zenodo.7890787.

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Purpose: In this paper highlights the recognition of hand-written Content/character problems that have been proposed. This study shows the emerging and widely used in many vision-based applications in recent years. In this work, the drawback of conventional handwriting recognition utilizes the statistical characteristics of individual Content/characters, and different subsets to recognize the different Content/characters are overcome. Design/Methodology/Approach: To mitigate this problem several works have been motivated to incorporate a deep learning model for handwriting recognition and achi

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Soundes, Mekki, and Labdaoui Ahlam. "A systematic study of autoencoder hyperparameters for effective feature learning in image recognition tasks: insights from handwriting dataset." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e12308. https://doi.org/10.54021/seesv5n2-812.

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This research investigates the potential of autoencoders to enhance handwritten digit recognition using the MNIST dataset. Autoencoders, with their encoding and decoding mechanisms, effectively capture essential data patterns, making them powerful tools for feature extraction and dimensionality reduction. The study evaluates various autoencoder architectures, including shallow and deep designs, by fine-tuning hyperparameters such as epochs, batch size, and learning rate to optimize model representations and improve recognition performance. Performance is measured using metrics like Mean Square

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Kusetogullari, Huseyin, Amir Yavariabdi, Abbas Cheddad, Håkan Grahn, and Johan Hall. "ARDIS: a Swedish historical handwritten digit dataset." Neural Computing and Applications 32, no. 21 (2019): 16505–18. http://dx.doi.org/10.1007/s00521-019-04163-3.

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Abstract This paper introduces a new image-based handwritten historical digit dataset named Arkiv Digital Sweden (ARDIS). The images in ARDIS dataset are extracted from 15,000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single-digit datasets and one-digit string dataset. The digit string dataset includes 10,000 samples in red–green–blue color space, whereas the other datasets contain 7600 single-digit images in different color spaces. An extensive analysis of

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Im, Sio-Kei, and Ka-Hou Chan. "Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU." Applied Sciences 15, no. 12 (2025): 6772. https://doi.org/10.3390/app15126772.

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Predicting the motion of handwritten digits in video sequences is challenging due to complex spatiotemporal dependencies, variable writing styles, and the need to preserve fine-grained visual details—all of which are essential for real-time handwriting recognition and digital learning applications. In this context, our study aims to develop a robust predictive framework that can accurately forecast digit trajectories while preserving structural integrity. To address these challenges, we propose a novel video prediction architecture integrating ConvCARU with a modified DCGAN to effectively sepa

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Nagadeepa.Ch, Dr.N.Balaji, and Dr.V.Padmaja. "ANALYSIS OF INERTIAL SENSOR DATA USING TRAJECTORY RECOGNITION ALGORITHM." International Journal on Cybernetics & Informatics (IJCI) 5, no. 4 (2017): 101–7. https://doi.org/10.5121/ijci.2016.5412.

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This paper describes a digital pen based on IMU sensor for gesture and handwritten digit gesture trajectory recognition applications. This project allows human and Pc interaction. Handwriting Recognition is mainly used for applications in the field of security and authentication. By using embedded pen the user can make hand gesture or write a digit and also an alphabetical character. The embedded pen contains an inertial sensor, microcontroller and a module having Zigbee wireless transmitter for creating handwriting and trajectories using gestures. The propound trajectory recognition algorithm

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Derdour, Khedidja, Hayet Mouss, and Rafik Bensaadi. "Multiple Features Extraction and Classifiers Combination Based Handwriting Digit Recognition." International Journal on Electrical Engineering and Informatics 13, no. 1 (2021): 163–78. http://dx.doi.org/10.15676/ijeei.2021.13.1.9.

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NG, GEOK SEE, SEVKI ERDOGAN, DAMING SHI, and ABDUL WAHAB. "INSIGHT OF FUZZY NEURAL SYSTEMS IN THE APPLICATION OF HANDWRITTEN DIGITS CLASSIFICATION." International Journal of Image and Graphics 06, no. 04 (2006): 511–32. http://dx.doi.org/10.1142/s0219467806002410.

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There have been many applications in the area of handwritten character recognition. Over the last decade much research has gone into developing algorithms to accurately convert captured images of handwriting to text. At the same time, research into neuro fuzzy classification models has proven to solve many complex problems. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) and Evolving Fuzzy Neural Network (EFuNN) was investigated and studied in detail on how these two models can be used to perform handwritten digits classification. Results of the experiments show great potential of

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AL-Saffar, Ahmed, Suryanti Awang, Wafaa AL-Saiagh, Ahmed Salih AL-Khaleefa, and Saad Adnan Abed. "A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN." Sensors 21, no. 21 (2021): 7306. http://dx.doi.org/10.3390/s21217306.

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Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the

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Onuean, Athita, Uraiwan Buatoom, Thatsanee Charoenporn, Taehong Kim, and Hanmin Jung. "Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset." Applied Sciences 12, no. 8 (2022): 4083. http://dx.doi.org/10.3390/app12084083.

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In handwriting recognition research, a public image dataset is necessary to evaluate algorithm correctness and runtime performance. Unfortunately, in existing Thai language script image datasets, there is a lack of variety of standard handwriting types. This paper focuses on a new offline Thai handwriting image dataset named Burapha-TH. The dataset has 68 character classes, 10 digit classes, and 320 syllable classes. For constructing the dataset, 1072 Thai native speakers wrote on collection datasheets that were then digitized using a 300 dpi scanner. De-skewing, detection box and segmentation

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Agrawal, A. K., A. K. Shrivas, and V. K. Awasthi. "An Improved and Customized Hybrid of Deep and Machine Learning Technique Model for Handwritten Digit Recognition." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, no. 01 SPL (2022): 13–19. http://dx.doi.org/10.18090/samriddhi.v14spli01.3.

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In modern research, pattern recognition is a vast field for the academicians and researchers to contribute their work. Various kinds of patterns like images, character, handwritten digit, etc can be recognized and classify with the help of the intelligent techniques. This research work is concentrated on the classification of handwritten digit recognition. In the world, peoples’ handwriting is different from each other’s and uses different languages, so it is necessary to develop a model that is able to recognize handwritten digits with high accuracy. Various deep and machine learning methods

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Ghanimi, Hayder M. A., N. Alagusundari, Jainabbi Banda, et al. "Linear discriminant analysis-based deep learning algorithms for numerical character handwriting recognition." Journal of Autonomous Intelligence 7, no. 5 (2024): 1621. http://dx.doi.org/10.32629/jai.v7i5.1621.

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<p>Information processing requires handwritten digit recognition, but methods of writing and image defects like brightness changes, blurring, and noise make image recognition challenging. This paper presents a strategy for categorizing offline handwritten digits in both Devanagari script and Roman script (English numbers) using Deep Learning (DL) algorithms, a branch of Machine Learning (ML) that uses Neural Networks (NN) with multiple layers to acquire hierarchical representations of input autonomously. The research study develops classification algorithms for recognising handwritten di

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Hossain, Md Shahadat, Md Anwar Hossain, AFM Zainul Abadin, and Md Manik Ahmed. "Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network." Engineering International 9, no. 2 (2021): 73–84. http://dx.doi.org/10.18034/ei.v9i2.551.

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The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition probl

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Kong, Yuanyuan. "Research and application of constructing image recognition models for archival handwriting." Journal of Computational Methods in Sciences and Engineering 25, no. 2 (2024): 1667–77. https://doi.org/10.1177/14727978241302442.

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Traditional file management mainly relies on manual processing, but with the development of artificial intelligence, the convolutional neural network (CNN) model has been widely used in the acquisition, processing, recognition, and conversion of handwritten text, which improves the efficiency and achieves accurate and fast information processing. In order to achieve high-precision handwritten digit recognition, this study proposes a high-performance model based on convolutional neural networks, which extracts features through a convolutional layer, under-samples using a maximum pooling layer t

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Naik, Saurav, Prathamesh Pathare, Muzammil Qureshi, Chirag Kalaswad, Akshat Joshi, and Nayan Paliwal. "Recognizing Handwritten Digits on MNIST Dataset using KNN Algorithm." Journal of Artificial Intelligence and Imaging 1, no. 2 (2024): 10–18. http://dx.doi.org/10.48001/joaii.2024.1210-18.

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Handwritten digit recognition is a critical job in computer vision and is used as a frequent benchmark for testing machine learning algorithms. This work describes the creation of a recognition system utilizing the K-Nearest Neighbors (KNN) method, which was chosen for its simplicity and ease of understanding. The system is built on the MNIST dataset, which contains a vast number of photographs of handwritten digits. The process begins with data collection and investigation, which involves analyzing the content and properties of the MNIST dataset to better comprehend the range of handwriting s

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Nisa, Kharisatun, Muhammad Ardi Hermansyah, Afilda Maharani Asmaraloka, Fikri Hamdhan Dwi Saputra, and Arif Setiawan. "Implementation of the K-Nearest Neighbor (KNN) Algorithm in Handwritten Digit Pattern Recognition Using the Zoning Method." Jurnal Ilmiah Sistem Informasi 4, no. 2 (2025): 175–85. https://doi.org/10.51903/kf6s5f56.

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Handwritten digit recognition is one of the key challenges in the field of digital image processing and artificial intelligence, with significant potential in various applications such as automatic form input systems, handwritten data correction, and attendance systems based on handwriting. This study aims to develop a web-based information system capable of automatically recognizing handwritten digits using the K-Nearest Neighbors (KNN) classification method. The system is designed through several main stages, including image preprocessing (conversion to grayscale, thresholding, and image siz

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Khunratchasana, Kheamparit, and Tassanan Treenuntharath. "Thai digit handwriting image classification with convolution neuron networks." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 110. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp110-117.

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This paper aims to determine the efficiency in classifying and recognizing Thai digit handwritten using convolutional neural networks (CNN). We created a new dataset called the Thai digit dataset. The performance test was divided into two parts: the first part determines the exact number of epochs, and the second part examines the occurrence of overfits in the model with Keras library's EarlyStoping() function, processed through Cloud Computing with Google Colaboratory, and used a Python programming language. The main parameters for the model were a dropout of 0.75, mini-batch size of 128, the

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Khunratchasana, Kheamparit, and Tassanan Treenuntharath. "Thai digit handwriting image classification with convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 110–17. https://doi.org/10.11591/ijeecs.v27.i1.pp110-117.

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This paper aims to determine the efficiency in classifying and recognizing Thai digit handwritten using convolutional neural networks (CNN). We created a new dataset called the Thai digit dataset. The performance test was divided into two parts: the first part determines the exact number of epochs, and the second part examines the occurrence of overfits in the model with Keras library's EarlyStoping() function, processed through cloud computing with Google Colaboratory, and used a Python programming language. The main parameters for the model were a dropout of 0.75, minibatch size of 128,

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He, Han, Xiaochen Chen, Adnan Mehmood, et al. "ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition." Sensors 20, no. 17 (2020): 4878. http://dx.doi.org/10.3390/s20174878.

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This paper introduces a prototype of ClothFace technology, a battery-free textile-based handwriting recognition platform that includes an e-textile antenna and a 10 × 10 array of radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. Touching the textile platform surface creates an electrical connection from specific ICs to the antenna, which enables the connected ICs to be read with an external UHF (ultra-haigh frequency) RFID reader. In this paper, the platform is demonstrated to recognize handwritten numbers 0–9. The raw data collected by the platform are a

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Qasim, Sarah Salman, and Safa Hussein Oleiwi. "Advancing Arabic Handwritten Digit Recognition with AI-Enhanced Neural Network Architectures." Babylonian Journal of Artificial Intelligence 2024 (November 28, 2024): 146–57. https://doi.org/10.58496/bjai/2024/016.

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Neural network model developed in this paper aims at classification of the hand written digits using the data set from Arabic Handwritten Digits Dataset (AHDD). It also includes data preprocessing, model design, training, validating, hyperparameter optimisation, and comparison methodologies of the project. Some preprocessing included scaling of pixel intensity and data augmentation to improve variation, as well as data separation between training and validation. proposed architecture of the model were updated through adding of dropout layers as a form of regularization, tuning of the quantity

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Viegas, Franklin Robert. "Enhancing Study Experience using Handwritten Character and Digit Recognition and Text Summarization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30665.

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The integration of Handwritten characters and, Digit Recognition and Deep Learning in education heralds a transformative era in learning methodologies. This abstract delves into the multifaceted benefits derived from the amalgamation of these technologies, redefining the educational landscape. Handwritten characters and Digit Recognition technology facilitates the seamless digitization of handwritten content, transcending the limitations of manual note-taking. Its introduction into educational frameworks enhances accessibility, promotes organization, and augments the searchability of diverse e

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Dzuba, Gregory, Alexander Filatov, Dmitry Gershuny, Igor Kil, and Vadim Nikitin. "Check Amount Recognition Based on the Cross Validation of Courtesy and Legal Amount Fields." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 04 (1997): 639–55. http://dx.doi.org/10.1142/s0218001497000275.

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Check amount recognition is one of the most promising commercial applications of handwriting recognition. This paper is devoted to the description of the check reading system developed to recognize amounts on American personal checks. Special attention is paid to a reliable procedure developed to reject doubtful answers. For this purpose the legal (worded) amount on a personal check is recognized along with the courtesy (digit) amount. For both courtesy and legal amount fields, a brief description of all recognition stages beginning with field extraction and ending with the recognition itself

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Gao, Yiting. "Handwritten digit recognition using machine learning." Applied and Computational Engineering 5, no. 1 (2023): 430–37. http://dx.doi.org/10.54254/2755-2721/5/20230613.

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Different people are expected to possess varying writing styles that are distinctive to their personalities. There are many aspects that make handwriting differ from person to person, which include spaces, inclination, height, basic patterns, connecting strokes, sizes and widths, markings, and ornaments, among others. However, the challenges come when the handwritten text must be converted into digital form to enhance information sharing and storage. To address this challenge, the recent past has seen the rise of reliance on machines over humans and the subsequent development of machine learni

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Shinde, Rajwardhan, Onkar Dherange, Rahul Gavhane, Hemant Koul, and Nilam Patil. "HANDWRITTEN MATHEMATICAL EQUATION SOLVER." International Journal of Engineering Applied Sciences and Technology 6, no. 10 (2022): 146–49. http://dx.doi.org/10.33564/ijeast.2022.v06i10.018.

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With recent developments in Artificial intelligence and deep learning every major field which is using computers for any type of work is trying to ease the work using deep learning methods. Deep learning is used in a wide range of fields due to its diverse range of applications like health, sports, robotics, education, etc. In deep learning, a Convolutional neural network (CNN) is being used in image classification, pattern recognition, Text classification, face recognition, live monitoring systems, handwriting recognition, Digit recognition, etc. In this paper, we propose a system for educati

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C, Dr Ramya. "Comparative Performance Evaluation of VGG-16 and Capsnet using Kannada MNIST." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1190–94. http://dx.doi.org/10.22214/ijraset.2021.37378.

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Abstract: Handwriting recognition is an important problem in character recognition. It is much more difficult especially for regional languages such as Kannada. In this regard there has been a recent surge of interest in designing convolutional neural networks (CNNs) for this problem. However, CNNs typically require large amounts of training data and cannot handle input transformations. Capsule networks, which is referred to as capsNets proposed recently to overcome these shortcomings and posed to revolutionize deep learning solutions. Our particular interest in this work is to recognize kanna

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Clements, Nicolle. "A Comparison of Simultaneous Confidence Intervals to Identify Handwritten Digits." International Journal of Business Intelligence Research 5, no. 3 (2014): 29–40. http://dx.doi.org/10.4018/ijbir.2014070103.

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This paper evaluates the use of several known simultaneous confidence interval methods for the automated recognition of handwritten digits from data in a well-known handwriting database. Contained in this database are handwritten digits, 0 through 9, that were obtained from 42,000 participants' writing samples. The objective of the analyses is to utilize statistical testing procedures that can be easily automated by a computer to recognize which digit was written by a subject. The methodologies discussed in this paper are designed to be sensitive to Type I errors and will control an overall me

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Wen, Yalin, Wei Ke, and Hao Sheng. "Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU." Applied Sciences 15, no. 1 (2024): 238. https://doi.org/10.3390/app15010238.

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Video location prediction for handwritten digits presents unique challenges in computer vision due to the complex spatiotemporal dependencies and the need to maintain digit legibility across predicted frames, while existing deep learning-based video prediction models have shown promise, they often struggle with preserving local details and typically achieve clear predictions for only a limited number of frames. In this paper, we present a novel video location prediction model based on Convolutional Gated Recurrent Units (ConvGRU) that specifically addresses these challenges in the context of h

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Sankhwar, Harshit, Kaushlendra Sahu, Nishant Keshav, and Deepak Mangal. "Handwritten Digit Classification Using CNN with MNIST Dataset." International Research Journal of Computer Science 11, no. 11 (2024): 671–76. https://doi.org/10.26562/irjcs.2024.v1111.07.

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Classifying handwritten digits is a crucial issue in computer vision and machine learning, and it serves as the foundation for many real-world applications like automated form processing, digitalizing postal addresses, and financial systems. A standard for assessing machine learning algorithms is the MNIST dataset, which consists of 70,000 grayscale pictures of handwritten numbers (0–9), with 60,000 for training and 10,000 for testing. A key problem in computer vision and machine learning is the classification of handwritten numbers, which forms the basis of numerous practical applications suc

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Jain, Parth Hasmukh, Vivek Kumar, Jim Samuel, Sushmita Singh, Abhinay Mannepalli, and Richard Anderson. "Customized AI Readers: An Adaptive Framework for Flexible Human Handwriting Recognition of Numerical Digits with OCR Methods." Information 14, no. 6 (2023): 305. http://dx.doi.org/10.3390/info14060305.

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Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy for general text. However, even after decades of intensive research, creating OCR with human-like abilities has remained evasive. One of the challenges has been that OCR models trained on general text do not perform well on localized or personalized handwritten text due to differen

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Wróbel, Michał, Janusz T. Starczewski, Justyna Fijałkowska, Agnieszka Siwocha, and Christian Napoli. "Handwritten Word Recognition Using Fuzzy Matching Degrees." Journal of Artificial Intelligence and Soft Computing Research 11, no. 3 (2021): 229–42. http://dx.doi.org/10.2478/jaiscr-2021-0014.

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Abstract Handwritten text recognition systems interpret the scanned script images as text composed of letters. In this paper, efficient offline methods using fuzzy degrees, as well as interval fuzzy degrees of type-2, are proposed to recognize letters beforehand decomposed into strokes. For such strokes, the first stage methods are used to create a set of hypotheses as to whether a group of strokes matches letter or digit patterns. Subsequently, the second-stage methods are employed to select the most promising set of hypotheses with the use of fuzzy degrees. In a primary version of the second

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Dagher, Issam, and Samir Abujamra. "Combined wavelet and Gabor convolution neural networks." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 06 (2019): 1950046. http://dx.doi.org/10.1142/s0219691319500462.

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Handwriting recognition is a very active research in the machine learning community. In this paper, we tackled two important applications: handwritten digit recognition and Signature verification using convolution neural network (CNN). Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. This paper focuses on offline signature verification (SV). It is a kind of a classification problem, which classifies the signature as genuine, or forgery. We use CNN in two types of datasets: the MNIST d

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Lu, Yuncong. "Handwritten capital letter recognition based on OpenCV." MATEC Web of Conferences 277 (2019): 02030. http://dx.doi.org/10.1051/matecconf/201927702030.

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Handwriting capitalization recognition is a function of distinguishing handwritten capital letters by means of machine or computer intelligence, which is classified into the field of optical character recognition. Given that capital letters are widely used around the world, identification and analysis are often used as the main components of some control systems. Therefore, the research on handwritten capital letter recognition is also very practical and has important practical significance. The key part of the research contained in this paper is the image preprocessing and the optimal selecti

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Deshpande, Mr Onkar. "Postal Address Identification and Sorting." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4946–53. http://dx.doi.org/10.22214/ijraset.2021.36023.

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In this fast-moving world, a normal man can take considerable time to find a postal card in a bunch of postcards with significant issues like unclear handwriting, having trouble recognizing some uncommon or ambiguous names. Also, in postal offices or industries, it negatively impacts the efficiency of the postal system. I am making a system for Indian postal automation based on recognizing pin-code on the postcard. In India, there are multiple languages were speak. Indian postcards are mainly written in three languages the state's official language, English, and Devanagari language. In India,

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Surendra, Kumar Shukla POONAM VERMA. "Using Convolutional Neural Networks, Arabic Handwritten Character Recognition." Scandinavian Journal of Information Systems 34, no. 2 (2023): 139–44. https://doi.org/10.5281/zenodo.7885780.

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Recognizing handwritten Arabic numbers is a challenging research topic. Impulsive by this research topic proposed two convolutional neural networks for recognizing Arabic handwritten numerals. Two proposed models have been analyzed using different filter sizes. The Arabic Number dataset exported from Kaggle was trained. The simplest proposed model achieved high recognition accuracy of 99.92%, outperforming the other complex with a more reasonable accuracy. For the MADBase dataset

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