We state that, one of the basic projects related to machine learning is “Handwritten Digit Recognition” and this project is mostly carried out the popular dataset named MNIST. Have a look at our projects that we have worked out and contact us for more enquires. Research ideas and research topics under Handwritten Digit Recognition Project using Machine Learning are completed successfully by our professional developers. Best solution will be given for an earliest and on time delivery for paper writing.
Our step-by-step procedure will assist us to execute and interpret handwritten digit recognition model by utilizing machine learning.
- Objective Description:
Describe the major aim: To create machine learning based framework for precisely recognize the handwritten digits.
- Collection of Data:
Dataset: Our approach mostly utilize MNIST dataset and it have 10,000 testing images and 60,000 training images and each has a dimension of 28×28 pixels and shows the (0-9) handwritten digits.
- Data Preprocessing:
Flattening: We transform the dimension of 28×28 pixel images into a flat vector with 784 values.
Normalization: For efficient training process, pixel values are normalized by us from [0, 255] to [0, 1].
- Model Chosen & development:
Conventional Machine Learning framework: Our project employs various methods like Support Vector Machine (SVM), Random Forest and K-Nearest neighbors (K-NN).
Deep Learning Models: For image data, we specifically utilize Convolutional Neural Networks (CNNs) and make use of libraries such as PyTorch and TensorFlow.
- Model Training:
Data Splitting: We divide the MNIST dataset into training and test dataset.
Training: In our work, the model is trained on training set. Utilize GPU acceleration for rapid training if we carried out our approach with CNN method.
Validation: We validate our framework by dividing the portion of training set into validation set if it is not offered in MNIST dataset.
- Model Evaluation:
To examine the framework’s efficiency, we make use of test set.
Metrics: For better understanding, our project considers the confusion matrix to know where the framework making errors and also considers accuracy metrics.
- Optimization and Hyperparameter Tuning (Optional):
We tune the framework’s hyperparameters like learning rate. For CNNs, count of layers is considered and for K-NN, count of neighbors is considered.
To avoid the overfitting issue in CNN, we employ methods such as dropout.
- 8. Deployment:
After we fulfilled with our framework’s efficiency, implement it in various platforms like as web service or mobile application where the users require the abilities of digit recognition.
- User Interface (if applicable):
To get an actual time forecasting, creation of easiest interface enables users to design or upload handwritten digits.
Through the use of gathered user-verified data, integrate review approach to periodically enhance our framework.
- Conclusion and Future Improvements:
Our research carried out the documentation of final results, possible applications and limitations.
We consider the following ideas in future improvement process:
To recognize the complicated handwritten patterns or cursive formats, we enhance our framework.
We deploy the abilities for actual-time recognition.
Retrain our framework with new data for continuous learning.
Tips:
Performing data augmentation process such as shifting, zooming and rotating of images assists to enhance our framework’s effectiveness.
Deep Learning methods specifically CNNs offers advanced efficiency on MNIST and other related tasks even when the conventional machine learning methods provides robust performance.
We demonstrate that, an effective handwritten digit recognition model utilized in various fields like bank check processing, postal mail sorting and other relevant fields where the handwritten digits require to be checked.
Handwritten Digit Recognition using Machine Learning Project Thesis Topics
The following are the thesis topics that we have supported on Handwritten Digit Recognition using Machine Learning concept know that research paper takes a lot of time and effort. Our team offers you the desired outcomes and prepare your PhD and MS thesis as per your guidelines and we also publish the paper in the reputable journal.
- Handwritten Digit Recognition Using CNN
Keywords:
CNN, MNIST dataset, Machine Learning, Handwritten Digit
Our paper displays the CNN classifier that can overcome the Neural Network with critical enhanced computational efficacy without handover execution. Using CNN from ML can recognize the handwritten digits. We utilize the dataset MNIST and compile with CNN that gives the structure of CNN to our project improvement.
- Dzongkha Handwritten Digit Recognition using Machine Learning Techniques
Keywords:
Dzongkha Language; Character Recognition; Digit Recognition; Handwritten Characters
The goal of our paper is to offer handwritten character recognition of Dzongkha digits by utilizing different ML methods. The prime motive behind our work is the unavailability of Dzongkha handwritten digit dataset. To enable the recognition of Dzongkha handwritten digit, we gather the data of Dzongkha handwritten digit from indigenous and non-indigenous people of Bhutan. We utilize the ML methods like SVM, KNN and DT. SVM gives the high accuracy rate.
- Handwritten Digit Recognition Using Machine Learning
Keywords:
Our work concentrates more and more on digitization and automation of nearly everything. So now it is important to get into the methods like Machine Learning, Artificial Intelligence, Deep Learning, etc. to classify and analyse the digits. We try to implement a method that recognizes the digits and make our work easier. We used CNN method to train our model.
- A Comparative Study of Handwritten Digit Recognition Algorithms
Keywords:
K Nearest Neighbour, Logistic Regression, MNIST, Decision Tree
Our work concentrates on finding handwritten digits namely numbers from 0 to 9 by utilizing the well-known MNIST dataset, that contains both test and train samples. We offer a comparative analyse of machine learning methods like CNN, DT, LR and KNN. We can preprocess the dataset and give input to the method and its metrics are determined and compare to test the efficacy of different methods.
- Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognition
Keywords:
Handwritten digit recognition, Feature extraction, Dimensionality reduction, Support vector machine
Our paper offer an efficient and robust low- dimensional representation of the digit image based on enhanced version of histogram of oriented gradient (HOG) as a feature descriptor. We also proposed PCA based dimensionality reduction to get features that optimize classification accuracy. The chosen sets of feature fed into RBF based SVM that perform classification and hyperparameters were optimized using Bayesian optimisation.
- Dimensionality Reduction in Handwritten Digit Recognition
Keywords:
Bangla handwritten digit recognition
Our work debates about dimensionality reduction method utilized in the Bangla Handwritten digit dataset NumtaDB. We examine the feature extraction methods like PCA, NCA and LDA. We classify the input automatically by utilizing CNN a DL method. CNN become good for classifying images for computer vision and recently it can utilize in other domains too. Then we classify the numeric digits by CNN that utilizes the lower dimension vector.
- Bangla Handwritten Digit Recognition
Keywords:
NumtaDB dataset, Deep CNN, Deep learning, Bangla digit recognition
We can preprocess the image by training DL methods with high accuracy is the goal of our paper. We used unbiased dataset, NumtaDB in our paper for Bangla digit recognition. Our paper utilizes different preprocessing methods that can be established for image processing including Deep CNN as classification method. Our NumtaDB dataset gives the best accuracy.
- Handwritten Digit Recognition of MNIST dataset using Deep Learning state-of-the-art Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)
Keywords:
Epochs, Hidden Layers, Stochastic Gradient Descent, Back propagation
We have to compare two major DL methods like Artificial Neural Network and Convolutional Neural Network and considering their feature extraction and classification stages of recognition. Our models were trained by utilizing categorical cross-entropy loss and ADAM optimizer on the MNIST dataset. We also used back propagation with Gradient Descent to train network with RELU activation that do automatic feature extraction.
- A Robust Model for Handwritten Digit Recognition using Machine and Deep Learning Technique
Keywords:
Image Preprocessing Techniques, Handwritten Digit Dataset, MNIST Handwritten Digit Dataset, Keras, RMSprop Optimizer
Our paper focuses on handwritten digit recognition for classification of patterns. We utilized the handwritten digit dataset named as MNIST contains several images. We utilized many ML and DL methods for handwritten digit recognition like SVM, RFC, KNN, MLP, CNN etc. We also suggested CNN as DL method on keras MNIST dataset and we compare the performance of CNN with SVM and KNN. Our proposed CNN based on keras can classify the handwritten digit image with RMSprop optimizer to optimize the model.
- A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition
Keywords:
Bangla OCR, handcrafted feature extraction, machine learning classifiers, HOG, LBP, Gabor
Our paper uses four difficult classifiers to recognize Bangla handwritten digit like KNN, SVM, RF and GBDT based on three handcrafted feature extraction techniques like Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Gabor filter on four publicly available datasets. We retrieve the features from dataset image by utilizing handcrafted feature extraction that can train ML classifier to find Bangla handwritten digits. Our HOG+SVM gives the best performance.