Handwritten digits recognition with decision tree classification: a machine learning approach

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9

Higher Order Centralised Scale-Invariants for Unconstrained Isolated Handwritten Digits

The works presented in this thesis are mainly involved in the study of global analysis of feature extractions. These include invariant moments for unequal scaling in x and y directions for handwritten digits, proposed method on scale-invariants and shearing invariants for unconstrained isolated handwritten digits. Classifications using Backpropagation model with its improved learning strategies are implemented in this study. Clustering technique with Self Organising Map (SOM) and dimension reduction with Principal Component Analysis (peA) on proposed invariant moments are also highlighted in this thesis. In feature extraction, a proposed improved formulation on scale-invariant moments is given mainly for unconstrained handwritten digits based on regular moments technique. Several types of features including algebraic and geometric invariants are also discussed. A computational comparison of these features found that the proposed method is superior than the existing feature techniques for unconstrained isolated handwritten digits. A proposed method on invariant moments with shearing parameters is also discussed. The formulation of this invariant shearing moments have been tested on unconstrained isolated handwritten digits. It is found that the proposed shearing moment invariants give good results for images which involved shearing parameters.peA is used in this study to reduce the dimension complexity of the proposed moments scale-invariants. The results show that the convergence rates of the proposed scaleinvariants are better after reduction process using peA. This implies that the peA is an alternative approach for dimension reduction of the moment invariants by using less variables for classification purposes. The results show that the memory storage can be saved by reducing the dimension of the moment invariants before sending them to the classifier. In addition, classifications of unconstrained isolated handwritten digits are extended using clustering technique with SOM methodology. The results of the study show that the clustering of the proposed moments scale-invariants is better visualised with SOM

NeuroWrite: Predictive Handwritten Digit Classification using Deep Neural Networks

The rapid evolution of deep neural networks has revolutionized the field of machine learning, enabling remarkable advancements in various domains. In this article, we introduce NeuroWrite, a unique method for predicting the categorization of handwritten digits using deep neural networks. Our model exhibits outstanding accuracy in identifying and categorising handwritten digits by utilising the strength of convolutional neural networks (CNNs) and recurrent neural networks (RNNs).In this article, we give a thorough examination of the data preparation methods, network design, and training methods used in NeuroWrite. By implementing state-of-the-art techniques, we showcase how NeuroWrite can achieve high classification accuracy and robust generalization on handwritten digit datasets, such as MNIST. Furthermore, we explore the model's potential for real-world applications, including digit recognition in digitized documents, signature verification, and automated postal code recognition. NeuroWrite is a useful tool for computer vision and pattern recognition because of its performance and adaptability.The architecture, training procedure, and evaluation metrics of NeuroWrite are covered in detail in this study, illustrating how it can improve a number of applications that call for handwritten digit classification. The outcomes show that NeuroWrite is a promising method for raising the bar for deep neural network-based handwritten digit recognition.Comment: 6 pages, 10 figure

Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models

This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models. A simple four qubit quantum circuit that uses Y rotation gates for encoding and two controlled NOT gates for creating correlation among the qubits is applied as a feature extraction filter prior to passing data into the fully connected NN architecture. By applying the QPF approach, the results show that the image classification accuracy based on the MNIST (handwritten 10 digits) and the EMNIST (handwritten 47 class digits and letters) datasets can be improved, from 92.5% to 95.4% and from 68.9% to 75.9%, respectively. These improvements were obtained without introducing extra model parameters or optimizations in the machine learning process. However, tests performed on the developed QPF approach against a relatively complex GTSRB dataset with 43 distinct class real-life traffic sign images showed a degradation in the classification accuracy. Considering this result, further research into the understanding and the design of a more suitable quantum circuit approach for image classification neural networks could be explored utilizing the baseline method proposed in this paper.Comment: 13 pages, 10 figure

Digit Classification of Majapahit Relic Inscription using GLCM-SVM

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %