3,300 research outputs found
Handwritten Digit Recognition Using Machine Learning Algorithms
Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition includes in postal mail sorting, bank check processing, form data entry, etc. The heart of the problem lies within the ability to develop an efficient algorithm that can recognize hand written digits and which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning technique. The main objective of this paper is to ensure effective and reliable approaches for recognition of handwritten digits. Several machines learning algorithm namely, Multilayer Perceptron, Support Vector Machine, NaFDA5; Bayes, Bayes Net, Random Forest, J48 and Random Tree has been used for the recognition of digits using WEKA. The result of this paper shows that highest 90.37% accuracy has been obtained for Multilayer Perceptron
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
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Handwritten Text Recognition (HTR) is still a challenging problem because it
must deal with two important difficulties: the variability among writing
styles, and the scarcity of labelled data. To alleviate such problems,
synthetic data generation and data augmentation are typically used to train HTR
systems. However, training with such data produces encouraging but still
inaccurate transcriptions in real words. In this paper, we propose an
unsupervised writer adaptation approach that is able to automatically adjust a
generic handwritten word recognizer, fully trained with synthetic fonts,
towards a new incoming writer. We have experimentally validated our proposal
using five different datasets, covering several challenges (i) the document
source: modern and historic samples, which may involve paper degradation
problems; (ii) different handwriting styles: single and multiple writer
collections; and (iii) language, which involves different character
combinations. Across these challenging collections, we show that our system is
able to maintain its performance, thus, it provides a practical and generic
approach to deal with new document collections without requiring any expensive
and tedious manual annotation step.Comment: Accepted to WACV 202
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