Handwritten Digit Recognition and Classification Using Machine Learning

In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy

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

uTHCD: A New Benchmarking for Tamil Handwritten OCR

Handwritten character recognition is a challenging research in the field of document image analysis over many decades due to numerous reasons such as large writing styles variation, inherent noise in data, expansive applications it offers, non-availability of benchmark databases etc. There has been considerable work reported in literature about creation of the database for several Indic scripts but the Tamil script is still in its infancy as it has been reported only in one database [5]. In this paper, we present the work done in the creation of an exhaustive and large unconstrained Tamil Handwritten Character Database (uTHCD). Database consists of around 91000 samples with nearly 600 samples in each of 156 classes. The database is a unified collection of both online and offline samples. Offline samples were collected by asking volunteers to write samples on a form inside a specified grid. For online samples, we made the volunteers write in a similar grid using a digital writing pad. The samples collected encompass a vast variety of writing styles, inherent distortions arising from offline scanning process viz stroke discontinuity, variable thickness of stroke, distortion etc. Algorithms which are resilient to such data can be practically deployed for real time applications. The samples were generated from around 650 native Tamil volunteers including school going kids, homemakers, university students and faculty. The isolated character database will be made publicly available as raw images and Hierarchical Data File (HDF) compressed file. With this database, we expect to set a new benchmark in Tamil handwritten character recognition and serve as a launchpad for many avenues in document image analysis domain. Paper also presents an ideal experimental set-up using the database on convolutional neural networks (CNN) with a baseline accuracy of 88% on test data.Comment: 30 pages, 18 figures, in IEEE Acces

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

State Of The Art In Digital Paleography

Digital paleography is an approach used to assist paleographers in deciding the origin of manuscripts. This is done by recording types of writings present in old manuscripts. It uses digital representation of book hands as a tool to support paleographical analyses by, human experts. There are six types of manuscripts selected which are Arabic, Chinese, Jawi, Indian, Latin and Roman. These types of manuscripts are discussed through their current contribution in the digital paleography field. The main purpose of this paper is to discuss the current work on digital paleography for selected types of manuscripts. Thus, we identified the approaches and methods used to define the types of handwritings in old manuscript

FEATURE EXTRACTION AND CLASSIFICATION THROUGH ENTROPY MEASURES

Entropy is a universal concept that represents the uncertainty of a series of random events. The notion \u201centropy" is differently understood in different disciplines. In physics, it represents the thermodynamical state variable; in statistics it measures the degree of disorder. On the other hand, in computer science, it is used as a powerful tool for measuring the regularity (or complexity) in signals or time series. In this work, we have studied entropy based features in the context of signal processing. The purpose of feature extraction is to select the relevant features from an entity. The type of features depends on the signal characteristics and classification purpose. Many real world signals are nonlinear and nonstationary and they contain information that cannot be described by time and frequency domain parameters, instead they might be described well by entropy. However, in practice, estimation of entropy suffers from some limitations and is highly dependent on series length. To reduce this dependence, we have proposed parametric estimation of various entropy indices and have derived analytical expressions (when possible) as well. Then we have studied the feasibility of parametric estimations of entropy measures on both synthetic and real signals. The entropy based features have been finally employed for classification problems related to clinical applications, activity recognition, and handwritten character recognition. Thus, from a methodological point of view our study deals with feature extraction, machine learning, and classification methods. The different versions of entropy measures are found in the literature for signals analysis. Among them, approximate entropy (ApEn), sample entropy (SampEn) followed by corrected conditional entropy (CcEn) are mostly used for physiological signals analysis. Recently, entropy features are used also for image segmentation. A related measure of entropy is Lempel-Ziv complexity (LZC), which measures the complexity of a time-series, signal, or sequences. The estimation of LZC also relies on the series length. In particular, in this study, analytical expressions have been derived for ApEn, SampEn, and CcEn of an auto-regressive (AR) models. It should be mentioned that AR models have been employed for maximum entropy spectral estimation since many years. The feasibility of parametric estimates of these entropy measures have been studied on both synthetic series and real data. In feasibility study, the agreement between numeral estimates of entropy and estimates obtained through a certain number of realizations of the AR model using Montecarlo simulations has been observed. This agreement or disagreement provides information about nonlinearity, nonstationarity, or nonGaussinaity presents in the series. In some classification problems, the probability of agreement or disagreement have been proved as one of the most relevant features. VII After feasibility study of the parametric entropy estimates, the entropy and related measures have been applied in heart rate and arterial blood pressure variability analysis. The use of entropy and related features have been proved more relevant in developing sleep classification, handwritten character recognition, and physical activity recognition systems. The novel methods for feature extraction researched in this thesis give a good classification or recognition accuracy, in many cases superior to the features reported in the literature of concerned application domains, even with less computational costs