9 research outputs found
Comparison on Logistic Regression, Random Forest, and CNN for Handwritten Digit Recognition
The technology of handwritten digit and character recognition is the process of identifying handwritten numbers using computers or other devices, machine train itself to recognize the handwritten digits or characters from various sources like bank cheque, mails, images, etc. This paper is about CNN, Logistic Regression, and Random Forest algorithm in handwritten digit or character recognition system, the system works on MNIST dataset for training and testing the models, to get the best accuracy this work rewrites CNN, Logistic Regression, and Random Forest with python libraries. Finally, these algorithms are analyzed by comparing the accuracy and recognition duration. Where CNN got better accuracy than other algorithm
An Online Numeral Recognition System Using Improved Structural Features – A Unified Method for Handwritten Arabic and Persian Numerals
With the advances in machine learning techniques, handwritten recognition systems also gained importance. Though digit recognition techniques have been established for online handwritten numerals, an optimized technique that is writer independent is still an open area of research. In this paper, we propose an enhanced unified method for the recognition of handwritten Arabic and Persian numerals using improved structural features. A total of 37 structural based features are extracted and Random Forest classifier is used to classify the numerals based on the extracted features. The results of the proposed approach are compared with other classifiers including Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN). Four different well-known Arabic and Persian databases are used to validate the proposed method. The obtained average 96.15% accuracy in recognition of handwritten digits shows that the proposed method is more efficient and produces better results as compared to other techniques
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