2 research outputs found
An Approach to Extract Features from Document Image for Character Recognition
In this paper we present a technique to extract features from a document image which can be used in machine learning algorithms in order to recognize characters from document image. The proposed method takes the scanned image of the handwritten character from paper document as input and processes that input through several stages to extract effective features. The object in the converted binary image is segmented from the background and resized in a global resolution. Morphological thinning operation is applied on the resized object and then the technique scanned the object in order to search for features there. In this approach the feature values are estimated by calculating the frequency of existence of some predefined shapes in a character object. All of these frequencies are considered as estimated feature values which are then stored in a vector. Every element in that vector is considered as a single feature value or an attribute for the corresponding image. Now these feature vectors for individual character objects can be used to train a suitable machine learning algorithms in order to classify a test object. The k-nearest neighbor classifier is used for simulation in this paper to classify the handwritten character into the recognized classes of characters. The proposed technique takes less time to compute, has less complexity and increases the performance of classifiers in matching the handwritten characters with the machine readable form
Hyperbox based machine learning algorithms: A comprehensive survey
With the rapid development of digital information, the data volume generated
by humans and machines is growing exponentially. Along with this trend, machine
learning algorithms have been formed and evolved continuously to discover new
information and knowledge from different data sources. Learning algorithms
using hyperboxes as fundamental representational and building blocks are a
branch of machine learning methods. These algorithms have enormous potential
for high scalability and online adaptation of predictors built using hyperbox
data representations to the dynamically changing environments and streaming
data. This paper aims to give a comprehensive survey of literature on
hyperbox-based machine learning models. In general, according to the
architecture and characteristic features of the resulting models, the existing
hyperbox-based learning algorithms may be grouped into three major categories:
fuzzy min-max neural networks, hyperbox-based hybrid models, and other
algorithms based on hyperbox representations. Within each of these groups, this
paper shows a brief description of the structure of models, associated learning
algorithms, and an analysis of their advantages and drawbacks. Main
applications of these hyperbox-based models to the real-world problems are also
described in this paper. Finally, we discuss some open problems and identify
potential future research directions in this field.Comment: 7 figure