1,766 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
Lossless compression with latent variable models
We develop a simple and elegant method for lossless compression using latent variable models, which we call `bits back with asymmetric numeral systems' (BB-ANS). The method involves interleaving encode and decode steps, and achieves an optimal rate when compressing batches of data. We demonstrate it rstly on the MNIST test set, showing that state-of-the-art lossless compression is possible using a small variational autoencoder (VAE) model. We then make use of a novel empirical insight, that fully convolutional generative models, trained on small images, are able to generalize to images of arbitrary size, and extend BB-ANS to hierarchical latent variable models, enabling state-of-the-art lossless compression of full-size colour images from the ImageNet dataset. We describe `Craystack', a modular software framework which we have developed for rapid prototyping of compression using deep generative models
An improved multiple classifier combination scheme for pattern classification
Combining multiple classifiers are considered as a new direction in the pattern recognition to improve classification performance. The main problem of multiple classifier combination is that there is no standard guideline for constructing an accurate and diverse classifier ensemble. This is due to the difficulty in identifying the number of homogeneous classifiers and how to combine the classifier outputs. The most commonly used ensemble method is the random strategy while the majority voting technique is used as the combiner. However, the random strategy cannot determine the number of classifiers and the majority voting technique does not consider the strength of each classifier, thus resulting in low classification accuracy. In this study, an improved multiple classifier combination scheme is proposed. The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. A compactness measure is introduced as a parameter in constructing an accurate and diverse classifier ensemble. A weighted voting technique is used to combine the classifier outputs by considering the strength of the classifiers prior to voting. Experiments were performed using four base classifiers, which are Nearest Mean Classifier (NMC), Naive Bayes Classifier (NBC), k-Nearest Neighbour (k-NN) and Linear Discriminant Analysis (LDA) on benchmark datasets, to test the credibility of the proposed multiple classifier combination scheme. The average classification accuracy of the homogeneous NMC, NBC, k-NN and LDA ensembles are 97.91%, 98.06%, 98.09% and 98.12% respectively. The accuracies are higher than those obtained through the use of other approaches in developing multiple classifier combination. The proposed multiple classifier combination scheme will help to develop other multiple classifier combination for pattern recognition and classification
Static and dynamic selection of ensemble of classifiers
Nous présentons dans cette thèse plusieurs solutions novatrices pour tenter de solutionner trois problèmes fondamentaux reliés à la conception des ensembles de classifieurs: la génération des classificateurs, la sélection et la fusion.
Une nouvelle fonction de fusion (Compound Diversity Function - CDF) basée sur la prise en compte de la performance individuelle des classificateurs et de la diversité entre pairs de classificateurs.
Une nouvelle fonction de fusion basée sur les matrices de confusions "pairwise" (PFM), mieux adaptée pour la fusion des classificateurs en présence d'un grand nombre de classes.
Une nouvelle méthode pour générer des ensembles de Mo- dèles de Markov Cachés (Hidden Markov Models - EoHMM) pour la reconnaissance des caractères manuscrits.
Une solution novatrice repose sur le concept des Oracles associés aux données de la base de validation (KNORA).
Une nouvelle approche pour la sélection des sous-espaces de représentation à partir d'une mesure de diversité évaluée entre les paires de partitions
Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey
Optical character recognition (OCR) is a vital process that involves the
extraction of handwritten or printed text from scanned or printed images,
converting it into a format that can be understood and processed by machines.
This enables further data processing activities such as searching and editing.
The automatic extraction of text through OCR plays a crucial role in digitizing
documents, enhancing productivity, improving accessibility, and preserving
historical records. This paper seeks to offer an exhaustive review of
contemporary applications, methodologies, and challenges associated with Arabic
Optical Character Recognition (OCR). A thorough analysis is conducted on
prevailing techniques utilized throughout the OCR process, with a dedicated
effort to discern the most efficacious approaches that demonstrate enhanced
outcomes. To ensure a thorough evaluation, a meticulous keyword-search
methodology is adopted, encompassing a comprehensive analysis of articles
relevant to Arabic OCR, including both backward and forward citation reviews.
In addition to presenting cutting-edge techniques and methods, this paper
critically identifies research gaps within the realm of Arabic OCR. By
highlighting these gaps, we shed light on potential areas for future
exploration and development, thereby guiding researchers toward promising
avenues in the field of Arabic OCR. The outcomes of this study provide valuable
insights for researchers, practitioners, and stakeholders involved in Arabic
OCR, ultimately fostering advancements in the field and facilitating the
creation of more accurate and efficient OCR systems for the Arabic language
Ensemble learning using multi-objective optimisation for arabic handwritten words
Arabic handwriting recognition is a dynamic and stimulating field of study within
pattern recognition. This system plays quite a significant part in today's global
environment. It is a widespread and computationally costly function due to cursive
writing, a massive number of words, and writing style. Based on the literature, the
existing features lack data supportive techniques and building geometric features.
Most ensemble learning approaches are based on the assumption of linear
combination, which is not valid due to differences in data types. Also, the existing
approaches of classifier generation do not support decision-making for selecting the
most suitable classifier, and it requires enabling multi-objective optimisation to handle
these differences in data types. In this thesis, new type of feature for handwriting using
Segments Interpolation (SI) to find the best fitting line in each of the windows with a
model for finding the best operating point window size for SI features. Multi-Objective
Ensemble Oriented (MOEO) formulated to control the classifier topology and provide
feedback support for changing the classifiers' topology and weights based on the
extension of Non-dominated Sorting Genetic Algorithm (NSGA-II). It is designated
as the Random Subset based Parents Selection (RSPS-NSGA-II) to handle neurons
and accuracy. Evaluation metrics from two perspectives classification and Multiobjective
optimization. The experimental design based on two subsets of the
IFN/ENIT database. The first one consists of 10 classes (C10) and 22 classes (C22).
The features were tested with Support Vector Machine (SVM) and Extreme Learning
Machine (ELM). This work improved due to the SI feature. SI shows a significant
result with SVM with 88.53% for C22. RSPS for C10 at k=2 achieved 91% accuracy
with fewer neurons than NSGA-II, and for C22 at k=10, accuracy has been increased
81% compared to NSGA-II 78%. Future work may consider introducing more features
to the system, applying them to other languages, and integrating it with sequence
learning for more accuracy
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