3,113 research outputs found
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
Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals
Interpretation of different writing styles, unconstrained cursiveness and
relationship between different primitive parts is an essential and challenging
task for recognition of handwritten characters. As feature representation is
inadequate, appropriate interpretation/description of handwritten characters
seems to be a challenging task. Although existing research in handwritten
characters is extensive, it still remains a challenge to get the effective
representation of characters in feature space. In this paper, we make an
attempt to circumvent these problems by proposing an approach that exploits the
robust graph representation and spectral graph embedding concept to
characterise and effectively represent handwritten characters, taking into
account writing styles, cursiveness and relationships. For corroboration of the
efficacy of the proposed method, extensive experiments were carried out on the
standard handwritten numeral Computer Vision Pattern Recognition, Unit of
Indian Statistical Institute Kolkata dataset. The experimental results
demonstrate promising findings, which can be used in future studies.Comment: 16 pages, 8 figure
Metaheuristic approach on feature extraction and classification algorithm for handwrittten character recognition
Handwritten Character Recognition (HCR) is a process of converting handwritten text into machine readable form and it comprises three stages; preprocessing, feature extraction and classification. This study acknowledged the issues regarding HCR performances particularly at the feature extraction and classification stages. In relation to feature extraction stage, the problem identified is related to continuous and minimum chain code feature extraction at its starting and revisit points due to branches of handwritten character. As for the classification stage, the problems identified are related to the input feature for classification that results in low accuracy of classification and classification model particularly in Artificial Neural Network (ANN) learning problem. Thus, the aim of this study is to extract the continuous chain code feature for handwritten character along with minimising its length and then proceed to develop and enhance the ANN classification model based on the extracted chain code in order to identify the handwritten character better. Four phases were involved in accomplishing the aim of this study. First, thinning algorithm was applied to remove the redundancies of pixel in handwritten character binary image. Second, graph based-metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature of the handwritten character image while minimising the route length of the chain code. Graph theory was then utilised as a solution representation. Hence, two metaheuristic approaches were adopted; Harmony Search Algorithm (HSA) and Flower Pollination Algorithm (FPA). As a result, HSA graphbased metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature for handwritten character. Based on the experiment conducted, it was demonstrated that the HSA graph-based metaheuristic feature extraction algorithm showed better performance in generating the shortest route length of chain code with minimum computational time compared to FPA. Furthermore, based on the evaluation of previous works, the proposed algorithm showed notable performance in terms of shortest route length of chain code for extracting handwritten character. Third, a feature vector was derived to address the input feature issue. The derivation of feature vector based on proposed formation rule namely Local Value Formation Rule (LVFR) and Global Value Formation Rule (GVFR) was adopted to create the image features for classification purpose. ANN was applied to classify the handwritten character based on the derived feature vector. Fourth, a hybrid of Firefly Algorithm (FA) and ANN (FA-ANN) classification model was proposed to solve the ANN network learning issue. Confusion Matrix was generated to evaluate the performance of the model in terms of precision, sensitivity, specificity, F-score, accuracy and error rate. As a result, the proposed hybrid FA-ANN classification model is superior in classifying the handwritten characters compared to the proposed feature vector-based ANN with 1.59 percent incremental in terms of accuracy model. Furthermore, the proposed hybrid FA-ANN also exhibits better performances compared to previous related works on HCR
Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition
The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%
Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology
Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/
Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology
Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/
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