Journal articles: 'Handwritten Bangla digit recognition'

1

Bhattacharjee, Indronil. "An Efficient Method for Bangla Handwritten Digit Recognition Using Convolutional Neural Network." Technium: Romanian Journal of Applied Sciences and Technology 18 (December 1, 2023): 65–74. http://dx.doi.org/10.47577/technium.v18i.10243.

Full text

Abstract:

Handwritten digit recognition is a fundamental problem in the field of computer vision and pattern recognition. This paper presents a Convolutional Neural Network (CNN) approach for recognizing handwritten Bangla digits. The proposed method utilizes a dataset of handwritten Bangla digit images and trains a CNN model to classify these digits accurately. The dataset is preprocessed to enhance the quality of the images and make them suitable for training the CNN model. The trained model is then tested on a separate test dataset to evaluate its performance in terms of accuracy. With the Ekush: Bangla Handwritten Data - Numerals dataset, we tested our CNN implementation to determine the precision of handwritten characters. According to the test results, 25% of the images using a training set of more than 150,000 images from Ekush dataset had an accuracy of 98.3%.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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 Bangla digits in real time using hand gestures in the air. As a result, this article describes the creation of a Bangla digit recognition model that employs a Convolution Neural Network (CNN) to predict Bangla digits by observing hand movements in the air space.  After a thorough examination, the suggested system attained a 98.37% accuracy on the BanglaLekha-Isolated dataset.

APA, Harvard, Vancouver, ISO, and other styles

3

Samanta, Roopkatha, Soulib Ghosh, Agneet Chatterjee, and Ram Sarkar. "A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays." International Journal of Computer Vision and Image Processing 10, no. 3 (2020): 1–17. http://dx.doi.org/10.4018/ijcvip.2020070101.

Full text

Abstract:

Due to the enormous application, handwritten digit recognition (HDR) has become an extremely important domain in optical character recognition (OCR)-related research. The predominant challenges faced in this domain include different photometric inconsistencies together with computational complexity. In this paper, the authors proposed a language invariant shape-based feature descriptor using the refraction property of light rays. It is to be noted that the proposed approach is novel as an adaptation of refraction property is completely new in this domain. The proposed method is assessed using five datasets of five different languages. Among the five datasets, four are offline (written Devanagari, Bangla, Arabic, and Telugu) and one is online (written in Assamese) handwritten digit datasets. The approach provides admirable outcomes for online digits whereas; it yields satisfactory results for offline handwritten digits. The method gives good result for both online and offline handwritten digits, which proves its robustness. It is also computationally less expensive compared to other state-of-the-art methods including deep learning-based models.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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 problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

APA, Harvard, Vancouver, ISO, and other styles

5

Shishir, Sarker, Sarker Songita, Rahman Sohanur, and Md. Ismail Jabiullah Dr. "A Lenet-5 Based Bangla Handwritten Digit Recognition Framework." Advancement in Image Processing and Pattern Recognition 2, no. 3 (2019): 1–7. https://doi.org/10.5281/zenodo.3564243.

Full text

Abstract:

<em>Hand composed Digit recognition in Bangla language is a valuable beginning stage for creating an Optical Character Recognition in the Bengali language. Be that as it may, Absence of huge and honest data collection, recognition of Bangla digit was not build already. In any case, in this outline, a colossal & honest data source known as NumtaDB is utilized for recognition of Bengali digits. The troublesome endeavour is connected to getting the solid presentation and high precision for gigantic, fair, common, natural and particularly extended NumtaDB dataset. So various sorts of pre-processing frameworks are utilized for planning pictures and a significant convolutional neural network is utilized for the request of representation in this paper. The LeNet-5 architecture based convolutional neural network model has indicated superb execution. We have accomplished 97.5% testing exactness which is a decent outcome for huge and fair NumtaDB dataset contrasting with other one-sided datasets. A wide range of pre-processing of pictures is additionally significant before preparing. We utilize some pre-processing strategies for obscure and loud pictures yet these are insufficient for the elite. An examination of the system brings out the EMNIST and MNIST datasets was performed so as to sustain the appraisal.</em>

APA, Harvard, Vancouver, ISO, and other styles

6

Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "A Study of Moment Based Features on Handwritten Digit Recognition." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/2796863.

Full text

Abstract:

Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. A lot of work has been done for various non-Indicscripts particularly, in case ofRoman, but, in case ofIndicscripts, the research is limited. This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely,Indo-Arabic,Bangla,Devanagari,Roman, andTelugu. A 130-element feature set which is basically a combination of six different types of moments, namely, geometric moment, moment invariant, affine moment invariant, Legendre moment, Zernike moment, and complex moment, has been estimated for each digit sample. Finally, the technique is evaluated onCMATERand MNIST databases using multiple classifiers and, after performing statistical significance tests, it is observed that Multilayer Perceptron (MLP) classifier outperforms the others. Satisfactory recognition accuracies are attained for all the five mentioned scripts.

APA, Harvard, Vancouver, ISO, and other styles

7

Hossain, Afsana, Md Sabbir Hasan, Md Mujtaba Asif, and Amit Kumar Das. "Performance Analysis On Bangla Handwritten Digit Recognition Using CNN And Transfer Learning." International Journal of Advanced Networking and Applications 13, no. 01 (2021): 4809–15. http://dx.doi.org/10.35444/ijana.2021.13101.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

De, Shankha, and Arpana Rawal. "BANGLA HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTION NEURAL NETWORK." ICTACT Journal on Soft Computing 12, no. 2 (2022): 2545–50. http://dx.doi.org/10.21917/ijsc.2022.0364.

Full text

Abstract:

Since, last one-decade, numerous deep learning models have been designed to resolve handwritten character recognition task in languages, namely, English, Chinese, Arabic, Japanese and Russian. Recognition of Bengali handwritten character from document image datasets is undoubtedly an open challenging task. Due to the advancement of neural network, many models have been developed and it is improving performance. The LeNet is a pioneering work in the field handwritten document image recognition specially hand written digits from the images by using CNN. This paper focuses on designing a convolution neural network with refinements on layers and its parameter tuning for Bengali character recognition system for classification of 50 different fonts. Our revised CNN model outperforms on some existing approach and shows font-recognition accuracy of 98.46%.

APA, Harvard, Vancouver, ISO, and other styles

9

Sarkhel, Ritesh, Nibaran Das, Amit K. Saha, and Mita Nasipuri. "A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition." Pattern Recognition 58 (October 2016): 172–89. http://dx.doi.org/10.1016/j.patcog.2016.04.010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Adila, Nuzhat, Tabassum Fahima, and Imdadul Islam Md. "Object Detection using Convolutional Neural Network and Extended SURF with FIS." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 918–25. https://doi.org/10.35940/ijeat.E9915.069520.

Full text

Abstract:

The aim of the paper is to detect object using the combination of three algorithms: convolutional neural network (CNN) and extended speeded up robust features (SUFR) and Fuzzy inference system (FIS). Here three types of objects are considered: first, we consider RGB images of hundred different types of objects (for example anchor, laptop airplane, car etc.) taken from benchmark database; second, we take grayscale images of human fingerprint from recognized database; third, Bangla handwritten alphabet from standard database. In this paper we extend the SURF algorithm then the result of the extended SURF is applied in FIS to enhance accuracy of detection. Finally, three algorithms are combined and the accuracy of detection of combined technique is found better than individual one. The combined algorithm provides the average recognition rate for objects of first case as 94.21%, for human finger print as 92.17%%, for Bangla letter as 92.38% and for the Bangla digit as 93.69%.

APA, Harvard, Vancouver, ISO, and other styles

11

Asraful, Md, Md Anwar Hossain, and Ebrahim Hossen. "Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach." Annals of Emerging Technologies in Computing 7, no. 3 (2023): 60–77. http://dx.doi.org/10.33166/aetic.2023.03.003.

Full text

Abstract:

Accurately classifying user-independent handwritten Bengali characters and numerals presents a formidable challenge in their recognition. This task becomes more complicated due to the inclusion of numerous complex-shaped compound characters and the fact that different authors employ diverse writing styles. Researchers have recently conducted significant researches using individual approaches to recognize handwritten Bangla digits, alphabets, and slightly compound characters. To address this, we propose a straightforward and lightweight convolutional neural network (CNN) framework to accurately categorize handwritten Bangla simple characters, compound characters, and numerals. The suggested approach exhibits outperformance in terms of performance when compared too many previously developed procedures, with faster execution times and requiring fewer epochs. Furthermore, this model applies to more than three datasets. Our proposed CNN-based model has achieved impressive validation accuracies on three datasets. Specifically, for the BanglaLekha isolated dataset, which includes 84-character classes, the validation accuracy was 92.48%. On the Ekush dataset, which includes 60-character classes, the model achieved a validation accuracy of 97.24%, while on the customized dataset, which includes 50-character classes, the validation accuracy was 97.03%. Our model has demonstrated high accuracy and outperformed several prominent existing frameworks.

APA, Harvard, Vancouver, ISO, and other styles

12

Cecotti, Hubert. "Hierarchical k-Nearest Neighbor with GPUs and a High Performance Cluster: Application to Handwritten Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 02 (2017): 1750005. http://dx.doi.org/10.1142/s0218001417500057.

Full text

Abstract:

The accelerating progress and availability of low cost computers, high speed networks, and software for high performance distributed computing allow us to reconsider computationally expensive techniques in image processing and pattern recognition. We propose a two-level hierarchical [Formula: see text]-nearest neighbor classifier where the first level uses graphics processor units (GPUs) and the second level uses a high performance cluster (HPC). The system is evaluated on the problem of character recognition with nine databases (Arabic digits, Indian digits (Bangla, Devnagari, and Oriya), Bangla characters, Indonesian characters, Arabic characters, Farsi characters and digits). Contrary to many approaches that tune the model for different scripts, the proposed image classification method is unchanged throughout the evaluation on the nine databases. We show that a hierarchical combination of decisions based on two distances, using GPUs and a HPC provides state-of-the-art performances on several scripts, and provides a better accuracy than more complex systems.

APA, Harvard, Vancouver, ISO, and other styles

13

Guha, Ritam, Manosij Ghosh, Pawan Kumar Singh, Ram Sarkar, and Mita Nasipuri. "M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification." Journal of Intelligent Systems 29, no. 1 (2019): 1453–67. http://dx.doi.org/10.1515/jisys-2019-0064.

Full text

Abstract:

Abstract The feature selection process is very important in the field of pattern recognition, which selects the informative features so as to reduce the curse of dimensionality, thus improving the overall classification accuracy. In this paper, a new feature selection approach named Memory-Based Histogram-Oriented Multi-objective Genetic Algorithm (M-HMOGA) is introduced to identify the informative feature subset to be used for a pattern classification problem. The proposed M-HMOGA approach is applied to two recently used feature sets, namely Mojette transform and Regional Weighted Run Length features. The experimentations are carried out on Bangla, Devanagari, and Roman numeral datasets, which are the three most popular scripts used in the Indian subcontinent. In-house Bangla and Devanagari script datasets and Competition on Handwritten Digit Recognition (HDRC) 2013 Roman numeral dataset are used for evaluating our model. Moreover, as proof of robustness, we have applied an innovative approach of using different datasets for training and testing. We have used in-house Bangla and Devanagari script datasets for training the model, and the trained model is then tested on Indian Statistical Institute numeral datasets. For Roman numerals, we have used the HDRC 2013 dataset for training and the Modified National Institute of Standards and Technology dataset for testing. Comparison of the results obtained by the proposed model with existing HMOGA and MOGA techniques clearly indicates the superiority of M-HMOGA over both of its ancestors. Moreover, use of K-nearest neighbor as well as multi-layer perceptron as classifiers speaks for the classifier-independent nature of M-HMOGA. The proposed M-HMOGA model uses only about 45–50% of the total feature set in order to achieve around 1% increase when the same datasets are partitioned for training-testing and a 2–3% increase in the classification ability while using only 35–45% features when different datasets are used for training-testing with respect to the situation when all the features are used for classification.

APA, Harvard, Vancouver, ISO, and other styles

14

Mujadded, Al Rabbani Alif. "State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture." State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture 9, no. 1 (2024): 11. https://doi.org/10.5281/zenodo.10538255.

Full text

Abstract:

Bangla Handwritten Character Recognition (HCR) remains a persistent challenge within the domain of Optical Character Recognition (OCR) systems. Despite extensive research efforts spanning several decades, achieving satisfactory success in this field has proven to be complicated. Bangla, being one of the most widely spoken languages worldwide, consists of 50 primary characters, including 11 vowels and 39 consonants. Unlike Latin languages, Bangla characters exhibit complex patterns, diverse sizes, significant variations, intricate letter shapes, and intricate edges. These characteristics further differ based on factors such as the writer's age and birthplace. In this paper, we propose a modified ResNet-34 architecture, a convolutional neural network (CNN) model, to identify Bangla handwritten characters accurately. The proposed approach is validated using a merged subset of two popular Bangla handwritten  datasets. Through our technique, we achieve state-of-the- art recognition performance. Experimental results  demonstrate that the suggested model attains an average accuracy of 98.70% for Bangla handwritten vowels, 97.34% for consonants, and 99.02% for numeric characters. Additionally, when applied to a mixed dataset comprising vowels, consonants, and numeric characters, the proposed model achieves an overall accuracy of 97%. This research contributes to advancing digital manufacturing systems by addressing the challenge of Bangla Handwritten Character Recognition, offering a high-performing solution based on a modified ResNet-34 architecture. The achieved recognition accuracy signifies significant progress in this field, potentially paving the way for enhanced automation and efficiency in various applications that involve processing Bangla handwritten text. Keywords:- Handwritten Character Recognition; ResNet; Optical Character Recognition; Computer Vision; Convolutional Neural Networks.

APA, Harvard, Vancouver, ISO, and other styles

15

Singh, Priyanshu, Pranali Pawar, and Nikhil Raj. "Handwritten Digit Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 75–82. http://dx.doi.org/10.22214/ijraset.2022.42062.

Full text

Abstract:

Abstract: Digital recognition is also remarkable an important issue. As handwritten digits are not a same size, thickness, position and direction, in this case by the way, various difficulties should be considered find the handwritten digital recognition problem. I unique and a variety of creative styles for different people moreover have an influence on the model as well the presence of digits. It is a strategy to see again edit written digits. It has a wide variety applications, for example, scheduled bank checks, post offices and tax documents and so on. The purpose of this project is to use the classification algorithm to identify handwritten digits. Background results are probably the most widely used Machine Learning Algorithms such as SVM, KNN and RFC and in-depth reading calculations like CNN multilayer using Keras and Theano and Tensorflow. Using these, 98.70% accuracy was used by CNN (Keras + Theano) compared to 97.91% using SVM, 96.67% using KNN, 96.89% using RFC was obtained. Keywords: SVM, RFC, KNN, CNN

APA, Harvard, Vancouver, ISO, and other styles

16

Saiteja, Jeisetti, Podila Srivally Rao, and P. Mani Bharadwaj. "HANDWRITTEN DIGIT RECOGNITION." International Journal of Computer Science and Mobile Computing 11, no. 1 (2022): 45–54. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.007.

Full text

Abstract:

This project dives into the fundamentals of machine learning victimization associate degree approachable and well-known artificial language, Python. And here we'll be reviewing 2 main components: 1st, we'll be learning regarding the aim of Machine Learning and wherever it applies to the important world. Second, we'll get a general summary of Machine learning topics like supervised learning, model analysis, and Machine Learning algorithms.

APA, Harvard, Vancouver, ISO, and other styles

17

Shinde, Jyoti, Chaitali Rajput, and Prof Mrunal Shidore Prof Milind Rane. "Handwritten Digit Recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 608–11. http://dx.doi.org/10.31142/ijtsrd8384.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Mane, Vijay, Ruta Sapate, Samruddhi Raut, Rohan Sonji, and Arya Khairnar. "Handwritten Digit Recognition." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 4878–82. http://dx.doi.org/10.22214/ijraset.2024.62557.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

19

Swarup, Kumar Supakar, Ali Mubasshir, Das Sutapa, and Singh Ekant. "Handwritten Digit Recognition." Advancement in Image Processing and Pattern Recognition 6, no. 3 (2023): 5–9. https://doi.org/10.5281/zenodo.7889214.

Full text

Abstract:

<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>

APA, Harvard, Vancouver, ISO, and other styles

20

Chaudhari, Tejas Prakash, Rajput Chaitali, and Mrunal Shidore |. Prof. Milind Rane Prof. "Handwritten Digit Recognition." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 608–11. https://doi.org/10.31142/ijtsrd8384.

Full text

Abstract:

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: https://www.ijtsrd.com/papers/ijtsrd8384.pdf

APA, Harvard, Vancouver, ISO, and other styles

21

Riasat, Azim, Fazlul Karim M., and Rahman Wahidur. "Bangla Hand Written Character Recognition Using Support Vector Machine." International Journal of Engineering Works (ISSN: 2409-2770) 3, no. 6 (2016): 36–46. https://doi.org/10.5281/zenodo.60329.

Full text

Abstract:

Recognizing handwritten character using computer is still consider a strong area of research. A fundamental problem in the field of Bangla character recognition is the lack of availability of Bangla handwritten character data set. In this thesis our main objective is to generate a larger dataset of Bangla character and as well as improving the recognition rate using Support Vector Machine. Support Vector Machines (SVM) is used for classification in pattern recognition widely. In our proposed method we applied support vector machine for increasing the recognition rate. A scanner is used to capture handwritten data sheet written in white paper by various people. After that several approaches used to generate the final data set for training and testing in SVM. A cropped image is scaled into 16*16 pixel matrix and then combing large number of image a dataset is produced. A binary classification technique of Support Vector Machine is implemented and rbf kernel function is used in SVM. This rbf SVM produces 93.43% overall recognition rate which is satisfactory result among all techniques applied on handwritten Bangla handwritten character recognition system.

APA, Harvard, Vancouver, ISO, and other styles

22

Bhattacharya, Nilanjana, Partha Pratim Roy, Umapada Pal, and Sanjit Kumar Setua. "ONLINE BANGLA HANDWRITTEN WORD RECOGNITION." Malaysian Journal of Computer Science 31, no. 4 (2018): 300–310. http://dx.doi.org/10.22452/mjcs.vol31no4.4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Naik, Dr Vishal, and Heli Mehta. "Comparison of Various Algorithms for Handwritten Character Recognition of Indian Languages." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 696–703. http://dx.doi.org/10.22214/ijraset.2023.56079.

Full text

Abstract:

Abstract: In this paper, we present a comparison of various pre-processor, feature extraction methods and algorithms for handwritten character recognition of various Indian languages. Comparison of classifier, feature set and accuracy of offline handwritten character recognition of Gujarati, Devanagari, Gurmukhi, Kannada, Malayalam, Bangla and Hindi Indian languages. Comparison of classifier, feature set and accuracy of online handwritten character recognition of Assamese, Tamil, Devanagari, Malayalam, Gurmukhi, and Bangla Indian languages. Indian language wise best performance of each language is compared for both offline and online handwritten character recognition systems.

APA, Harvard, Vancouver, ISO, and other styles

24

Abdleazeem, Sherif, and Ezzat El-Sherif. "Arabic handwritten digit recognition." International Journal of Document Analysis and Recognition (IJDAR) 11, no. 3 (2008): 127–41. http://dx.doi.org/10.1007/s10032-008-0073-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

A, Mahesh, Kanagaraj D, Thirugnanam G, Vinoth Kumar, Ukesh Kumar, and Velmurugan A. "Handwritten Digit Recognition using Machine Learning with Python." International Research Journal of Computer Science 10, no. 05 (2023): 168–71. http://dx.doi.org/10.26562/irjcs.2023.v1005.11.

Full text

Abstract:

Handwritten Digit Recognition is one of the essentially significant issues in pattern recognition applications. The main purpose of this project is to build an automatic handwritten digit recognition method for the recognition of handwritten digit strings. This paper proposes a simple convolution neural network approach to handwritten digit recognition. Convolutional Neural Network model is implemented using MNIST dataset. This dataset consists 60,000 small square 28×28pixel grayscale images of handwritten single digits between 0 and 9. The applications of digit recognition include postal mail sorting, check processing, form data entry, etc. The core of the issue exists in the capacity to foster a proficient calculation that can perceive manually written digits and which is put together by clients by the method of a scanner, tablet, and other computerized gadgets.

APA, Harvard, Vancouver, ISO, and other styles

26

Sethy, Abhisek, Prashanta Kumar Patra, and Soumya Ranjan Nayak. "A Hybrid System for Handwritten Character Recognition with High Robustness." Traitement du Signal 39, no. 2 (2022): 567–76. http://dx.doi.org/10.18280/ts.390218.

Full text

Abstract:

In the past few decades, the offline recognition of handwritten Indic scripts has received much attention of researchers. Although an intensive research has been reported for various Indic languages, limited research work is carried out for handwritten Odia, Bangla character recognition owing to their complex shapes and the unavailability of the standard datasets. This paper proposes an automated model for recognizing both handwritten of Odia characters and numerals, along with Bangla numerals with maximum optimization efficiency. The proposed model primarily deals with feature optimization parameters which mainly comprises of three parts. Firstly, the fast discrete curvelet transform (FDCT) is used to derive multidirectional features from the character images. Secondly, PCA along with LDA is used to reduce the dimension of the feature vector. The features are finally subjected to both least-squares support vector machine (LS-SVM), and random forest (RF) for classification. The effectiveness of proposed model is evaluated over three benchmark datasets such as Odia handwritten character, Bangla numeral and Odia handwritten numeral. The efficacy of proposed model achieves superiority as compared to state-of-art techniques. The discriminatory prospective of FDCT along with PCA and LDA features is establish more suitable than its counterparts.

APA, Harvard, Vancouver, ISO, and other styles

27

S, Nithyapriya, Shanthini C, Suvalaxmi S, and Shalini P. "Handwritten Digit Recognition Using CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25942.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

28

Hossain, Md Anwar, AFM Zainul Abadin, Md Omar Faruk, et al. "Bangla handwritten word recognition using YOLO V5." Bulletin of Electrical Engineering and Informatics 13, no. 3 (2024): 2175–90. http://dx.doi.org/10.11591/eei.v13i3.6953.

Full text

Abstract:

This research paper presents an innovative solution for offline handwritten word recognition in Bengali, a prominent Indic language. The complexities of this script, particularly in cursive writing, often lead to overlapping characters and segmentation challenges. Conventional methodologies, reliant on individual character recognition and aggregation, are error-prone. To overcome these limitations, we propose a novel method treating the entire document as a coherent entity and utilizing the efficient you only look once (YOLO) model for word extraction. In our approach, we view individual words as distinct objects and employ the YOLO model for supervised learning, transforming object detection into a regression problematic to predict spatially detached bounding boxes and class possibilities. Rigorous training results in outstanding performance, with remarkable box_loss of 0.014, obj_loss of 0.14, and class_loss of 0.009. Furthermore, the achieved mAP_0.5 score of 0.95 and map_0.5:0.95 score of 0.97 demonstrates the model’s exceptional accuracy in detecting and recognizing handwritten words. To evaluate our method comprehensively, we introduce the Omor-Ekush dataset, a meticulously curated collection of 21,300 handwritten words from 150 participants, featuring 141 words per document. Our pioneering YOLO-based approach, combined with the curated Omor-Ekush dataset, represents a significant advancement in handwritten word recognition in Bengali.

APA, Harvard, Vancouver, ISO, and other styles

29

Shibly, Mir Moynuddin Ahmed, Tahmina Akter Tisha, Tanzina Akter Tani, and Shamim Ripon. "Convolutional neural network-based ensemble methods to recognize Bangla handwritten character." PeerJ Computer Science 7 (June 28, 2021): e565. http://dx.doi.org/10.7717/peerj-cs.565.

Full text

Abstract:

In this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-office automation, signboard recognition, and many more. A large-scale and efficient isolated Bangla handwritten character classifier can be the first building block to create such a system. This study aims to classify the handwritten Bangla characters. The proposed methods of this study are divided into three phases. In the first phase, seven convolutional neural networks i.e., CNN-based architectures are created. After that, the best performing CNN model is identified, and it is used as a feature extractor. Classifiers are then obtained by using shallow machine learning algorithms. In the last phase, five ensemble methods have been used to achieve better performance in the classification task. To systematically assess the outcomes of this study, a comparative analysis of the performances has also been carried out. Among all the methods, the stacked generalization ensemble method has achieved better performance than the other implemented methods. It has obtained accuracy, precision, and recall of 98.68%, 98.69%, and 98.68%, respectively on the Ekush dataset. Moreover, the use of CNN architectures and ensemble methods in large-scale Bangla handwritten character recognition has also been justified by obtaining consistent results on the BanglaLekha-Isolated dataset. Such efficient systems can move the handwritten recognition to the next level so that the handwriting can easily be automated.

APA, Harvard, Vancouver, ISO, and other styles

30

Fukushima, Kunihiko. "Neocognitron for handwritten digit recognition." Neurocomputing 51 (April 2003): 161–80. http://dx.doi.org/10.1016/s0925-2312(02)00614-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

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.

Full text

Abstract:

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 problem with numerous real-life applications. Accurate recognition of various handwriting styles, specifically digits is a task studied for decades and this paper summarizes the achieved results. The best results have been achieved with convolutional neural networks while the worst methods are linear classifiers. The convolutional neural networks give better results if the dataset is expended with data augmentation.

APA, Harvard, Vancouver, ISO, and other styles

32

Amandeep, Dr. "Handwritten Digit Recognition using CNN and Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51002.

Full text

Abstract:

The use of intelligent systems has become much more common in recent years. Among these, handwritten digit recognition has become a key problem in the field of artificial intelligence and pattern recognition. This study examines the creation and assessment of models for handwritten numerical digit recognition using the MNIST dataset. SVM, MLP, and CNN are the three techniques. We also examine the effects of network width (number of filters per layer), depth (number of convolutional layers), and activation functions (such as ReLU, Tanh, and Sigmoid) on the learning capacity and generalisation ability of the CNN models. CNNs are useful for large-scale handwritten digit classification jobs because, according to experimental data, they provide recognition accuracy and faster processing times than typical machine learning models. In this project their is the use of CNNs and deep learning for handwritten digit recognition. We examine the fundamental architecture of CNNs, which consists of fully connected layers for final classification and decision-making, pooling layers to reduce the dimensions and translational invariance and convolutional layer for feature extraction using filters. Keywords Handwritten Digit Recognition, MNIST Dataset, Convolutional Neural Network (CNN), Support Vector Machine (SVM), Multi- Layer Perceptron (MLP), Deep Learning, Machine Learning.

APA, Harvard, Vancouver, ISO, and other styles

33

Tyagi, Tannu. "Handwritten Digit Recognition System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 3150–55. http://dx.doi.org/10.22214/ijraset.2024.62254.

Full text

Abstract:

Abstract: Handwritten digit recognition remains a crucial area of research in pattern recognition and machine learning. In this paper, we present a novel approach to enhance handwritten digit recognition systems by incorporating deep learning techniques and an interactive graphical user interface (GUI). Our system employs convolutional neural networks (CNNs) for feature extraction and classification, allowing for improved accuracy and robustness in digit recognition tasks.

APA, Harvard, Vancouver, ISO, and other styles

34

Wen, Ying, and Lianghua He. "A classifier for Bangla handwritten numeral recognition." Expert Systems with Applications 39, no. 1 (2012): 948–53. http://dx.doi.org/10.1016/j.eswa.2011.07.092.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Ali Nur, Mukerem, Mesfin Abebe, and Rajesh Sharma Rajendran. "Handwritten Geez Digit Recognition Using Deep Learning." Applied Computational Intelligence and Soft Computing 2022 (November 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/8515810.

Full text

Abstract:

Amharic language is the second most spoken language in the Semitic family after Arabic. In Ethiopia and neighboring countries more than 100 million people speak the Amharic language. There are many historical documents that are written using the Geez script. Digitizing historical handwritten documents and recognizing handwritten characters is essential to preserving valuable documents. Handwritten digit recognition is one of the tasks of digitizing handwritten documents from different sources. Currently, handwritten Geez digit recognition researches are very few, and there is no available organized dataset for the public researchers. Convolutional neural network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. In this work, the proposed model is to recognize Geez digits using CNN. Deep neural networks, which have recently shown exceptional performance in numerous pattern recognition and machine learning applications, are used to recognize handwritten Geez digits, but this has not been attempted for Ethiopic scripts. Our dataset, which contains 51,952 images of handwritten Geez digits collected from 524 individuals, is used to train and evaluate the CNN model. The application of the CNN improves the performance of several machine-learning classification methods significantly. Our proposed CNN model has an accuracy of 96.21% and a loss of 0.2013. In comparison to earlier research works on Geez handwritten digit recognition, the study was able to attain higher recognition accuracy using the developed CNN model.

APA, Harvard, Vancouver, ISO, and other styles

36

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.

Full text

Abstract:

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 one of the important areas research and development with a range of possibilities that could be achieved. Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is a capability computers to receive and interpret comprehensible handwritten input from sources such as paper documents, photos, touch screen

APA, Harvard, Vancouver, ISO, and other styles

37

Jiang, Qisheng. "A Financial Handwritten Digit Recognition Model Based on Artificial Intelligence." Frontiers in Computing and Intelligent Systems 2, no. 2 (2023): 70–74. http://dx.doi.org/10.54097/fcis.v2i2.4146.

Full text

Abstract:

Handwritten digit recognition is a kind of image information classification problem through optical character analysis. Its essence is to represent the pixel points of digital images as gray values and then replace the pixel matrix with a numerical matrix. The computer can deal with numerical problems converted from handwritten Arabic numeral recognition problems through feature extraction and classification. With the rapid development of science and technology, this technology has dramatically reduced the cost of identification and human consumption, making identification more efficient and having a specific use value. However, the current handwritten digit recognition technology will cause problems such as abnormal recognition and recognition errors, reducing recognition accuracy. This will not only increase the cost of human recognition but also increase unnecessary risks. Based on the broad application prospect of handwritten digit recognition in finance, this paper focuses on the research and analysis of the handwritten digit recognition model for its insufficient accuracy, low performance, and other problems. Based on traditional data analysis, this paper adopts deep learning and control variable methods to conduct multiple groups of experiments to explore the impact of different parameters on the accuracy of experimental results. Summarize the best recognition accuracy and achieve the best model performance.

APA, Harvard, Vancouver, ISO, and other styles

38

Mukhoti, Jishnu, Sukanya Dutta, and Ram Sarkar. "Handwritten Digit Classification in Bangla and Hindi Using Deep Learning." Applied Artificial Intelligence 34, no. 14 (2020): 1074–99. http://dx.doi.org/10.1080/08839514.2020.1804228.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Yoon, Sung-Soo, Hyun-Sook Chung, Kwang-Oh Yi, Yill-Byeong Lee, and Sang-Ho Lee. "A Study on Human Recognition Experiments with Handwritten Digit for Machine Recognition of Handwritten Digit." Journal of Korean Institute of Intelligent Systems 18, no. 3 (2008): 373–80. http://dx.doi.org/10.5391/jkiis.2008.18.3.373.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Dogra, Shallu, Ishav Mehra, Lavish Pathak, and Nishant Nishant. "Handwritten Digit Recognition by Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem37840.

Full text

Abstract:

This paper evaluates recent advances in handwritten digit recognition models, focusing on strategies developed and deployed in practical applications. The project utilizes both traditional and deep learning approaches, employing architectures such as Convolutional Neural Networks (CNNs). This paper explores the comparative performance of various models, discusses their deployment in real-world scenarios, and highlights future prospects for enhancing handwritten digit recognition technology. Keywords: Handwritten Digit Recognition, Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), AlexNet, Deep Learning for Image Classification, Neural Networks, Image Preprocessing, Data Augmentation, Feature Extraction, Image to Grayscale Conversion, Image Normalization, Training and Validation, Accuracy Metrics, Model Evaluation, Transfer Learning, Classification Algorithms, Real- Time Processing, Computer Vision, Image Recognition.

APA, Harvard, Vancouver, ISO, and other styles

41

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.

Full text

Abstract:

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 Recognition, Deep Learning, CNN, and Computational Intelligence are key terms.

APA, Harvard, Vancouver, ISO, and other styles

42

Mishra, Piyush. "Hand Written Digit Recognition System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 3983–85. http://dx.doi.org/10.22214/ijraset.2024.62449.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

43

Raja, Hiral, Aarti Gupta, and Rohit Miri. "Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques." European Journal of Engineering and Technology Research 6, no. 4 (2021): 37–44. http://dx.doi.org/10.24018/ejers.2021.6.4.2460.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

44

Raja, Hiral, Aarti Gupta, and Rohit Miri. "Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques." European Journal of Engineering and Technology Research 6, no. 4 (2021): 37–44. http://dx.doi.org/10.24018/ejeng.2021.6.4.2460.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

45

Li, Yifan. "Handwritten Digit Recognition Based on TensorFlow Framework." Applied and Computational Engineering 157, no. 1 (2025): 172–78. https://doi.org/10.54254/2755-2721/2025.po24694.

Full text

Abstract:

This study investigates the widespread application of convolutional neural networks (CNNs) in the field of handwritten digit recognition. To address the challenge of style variability in handwritten digit datasets, an efficient recognition model is proposed. The model is developed using the TensorFlow framework and enhances feature extraction and classification performance through the integration of convolutional layers, pooling layers, ReLU activation functions, batch normalization, and dropout techniques. The MNIST dataset is employed for experimentation. The model is trained using the Adam optimizer and achieves an accuracy of 98.75%, demonstrating the high effectiveness of CNNs in handwritten digit recognition. Furthermore, the study examines the models limitations in recognizing dynamically sized images and complex mathematical expressions and proposes potential improvements, such as learning rate adjustment and image size standardization.

APA, Harvard, Vancouver, ISO, and other styles

46

Tan, Yuxing, and Hongge Yao. "Deep Capsule Network Handwritten Digit Recognition." International Journal of Advanced Network, Monitoring and Controls 5, no. 4 (2021): 1–8. http://dx.doi.org/10.21307/ijanmc-2020-031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Albahli, Saleh, Fatimah Alhassan, Waleed Albattah, and Rehan Ullah Khan. "Handwritten Digit Recognition: Hyperparameters-Based Analysis." Applied Sciences 10, no. 17 (2020): 5988. http://dx.doi.org/10.3390/app10175988.

Full text

Abstract:

Neural networks have several useful applications in machine learning. However, benefiting from the neural-network architecture can be tricky in some instances due to the large number of parameters that can influence performance. In general, given a particular dataset, a data scientist cannot do much to improve the efficiency of the model. However, by tuning certain hyperparameters, the model’s accuracy and time of execution can be improved. Hence, it is of utmost importance to select the optimal values of hyperparameters. Choosing the optimal values of hyperparameters requires experience and mastery of the machine learning paradigm. In this paper, neural network-based architectures are tested based on altering the values of hyperparameters for handwritten-based digit recognition. Various neural network-based models are used to analyze different aspects of the same, primarily accuracy based on hyperparameter values. The extensive experimentation setup in this article should, therefore, provide the most accurate and time-efficient solution models. Such an evaluation will help in selecting the optimized values of hyperparameters for similar tasks.

APA, Harvard, Vancouver, ISO, and other styles

48

Das, Ankita, Tuhin Kundu, and Chandran Saravanan. "Dimensionality Reduction for Handwritten Digit Recognition." EAI Endorsed Transactions on Cloud Systems 4, no. 13 (2018): 156590. http://dx.doi.org/10.4108/eai.12-2-2019.156590.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

SASAO, Tsutomu, Yuto HORIKAWA, and Yukihiro IGUCHI. "Classification Functions for Handwritten Digit Recognition." IEICE Transactions on Information and Systems E104.D, no. 8 (2021): 1076–82. http://dx.doi.org/10.1587/transinf.2020lop0002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Saradhi, V. Vijaya, and M. Narasimha Murty. "Bootstrapping for efficient handwritten digit recognition." Pattern Recognition 34, no. 5 (2001): 1047–56. http://dx.doi.org/10.1016/s0031-3203(00)00043-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Handwritten Bangla digit recognition'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles