Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition

Khandy, Owais Mujtaba; Dadvandipour, Samad

doi:10.11591/ijeecs.v21.i1.pp574-581

Cited by 8 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last two approaches, incorporating bagging techniques, boosting techniques, and random subspace principles, have been used and have proven very successful. This algorithm works with two parameters, L and K. L is the number of trees in the forest, and features K are preselected for the splitting process [33].…”

Section: )mentioning

confidence: 99%

Handwritten Character Recognition using Deep Learning Algorithm with Machine Learning Classifier

Liman,

Josef,

Kusuma

2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

Handwritten character recognition is a problem that has been worked on for many mainstream languages. Handwritten letter recognition has been proven to achieve promising results. Several studies using deep learning models have been conducted to achieve better accuracies. In this paper, the authors conducted two experiments on the EMNIST Letters dataset: Wavemix-Lite and CoAtNet. The Wavemix-Lite model uses Two-Dimensional Discrete Wavelet Transform Level 1 to reduce the parameters and speed up the runtime. The CoAtNet is a combined model of CNN and Visual Transformer where the image is broken down into fixed-size patches. The feature extraction part of the model is used to embed the input image into a feature vector. From those two models, the authors hooked the value of the features of the Global Average Pool layer using EMNIST Letters data. The features hooked from the training results of the two models, such as SVM, Random Forest, and XGBoost models, were used to train the machine learning classifier. The experiments conducted by the authors show that the best machine-learning model is the Random Forest, with 96.03% accuracy using the Wavemix-Lite model and 97.90% accuracy using the CoAtNet model. These results showcased the benefit of using a machine learning model for classifying image features that are extracted using a deep learning model.

show abstract

Section: )mentioning

confidence: 99%

Handwritten Character Recognition using Deep Learning Algorithm with Machine Learning Classifier

Liman,

Josef,

Kusuma

2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms, especially classification algorithms are utilized for similar to human activity recognition. Some of the significant problems addressed using the machine learning algorithms are: detecting malicious links from world wide web (www) [21], handwritten digit recognition [22], depression detection from image and video analysis [23], air temperature prediction [24], etc. Therefore, we have applied seven different classification algorithms namely, logistic regression, random forest, K-nearest neighbor (k-NN), Support vector machine (SVM), Gradient Boosting, Convolutional Neural Network (CNN), Bi-directional LSTM.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Алам

Das

Tasjid

et al. 2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

<p class="0abstract">Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.<strong></strong></p>

show abstract

Novel Approach for Character Recognition Using Chi-Square

Indarapu

Soora

Waseem

et al. 2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition

Cited by 8 publications

References 22 publications

Handwritten Character Recognition using Deep Learning Algorithm with Machine Learning Classifier

Handwritten Character Recognition using Deep Learning Algorithm with Machine Learning Classifier

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Novel Approach for Character Recognition Using Chi-Square

Contact Info

Product

Resources

About