A Framework for the Selection of Binarization Techniques on Palm Leaf Manuscripts Using Support Vector Machine
نویسندگان
چکیده
Challenges for text processing in ancient document images are mainly due to the high degree of variations in foreground and background. Image binarization is an image segmentation technique used to separate the image into text and background components. Although several techniques for binarizing text documents have been proposed, the performance of these techniques varies and depends on the image characteristics.Therefore, selecting binarization techniques can be a key idea to achieve improved results. This paper proposes a framework for selecting binarizing techniques of palm leaf manuscripts using Support Vector Machines (SVMs).The overall process is divided into three steps: (i) feature extraction: feature patterns are extracted from grayscale images based on global intensity, local contrast, and intensity; (ii) treatment of imbalanced data: imbalanced dataset is balanced by using Synthetic Minority Oversampling Technique as to improve the performance of prediction; and (iii) selection: SVM is applied in order to select the appropriate binarization techniques. The proposed framework has been evaluated with palm leaf manuscript images and benchmarking dataset from DIBCO series and compared the performance of prediction between imbalanced and balanced datasets. Experimental results showed that the proposed framework can be used as an integral part of an automatic selection process.
منابع مشابه
Carbon Monoxide Prediction in the Atmosphere of Tehran Using Developed Support Vector Machine
Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily C...
متن کاملFeature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measu...
متن کاملSustainable Supplier Selection by a New Hybrid Support Vector-model based on the Cuckoo Optimization Algorithm
For assessing and selecting sustainable suppliers, this study considers a triple-bottom-line approach, including profit, people and planet, and regards business operations, environmental effects along with social responsibilities of the suppliers. Diverse metrics are acquainted with measure execution in these three issues. This study builds up a new hybrid intelligent model, namely COA-LS-SVM, ...
متن کاملFault diagnosis in a distillation column using a support vector machine based classifier
Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...
متن کاملAn Intelligence-Based Model for Supplier Selection Integrating Data Envelopment Analysis and Support Vector Machine
The importance of supplier selection is nowadays highlighted more than ever as companies have realized that efficient supplier selection can significantly improve the performance of their supply chain. In this paper, an integrated model that applies Data Envelopment Analysis (DEA) and Support Vector Machine (SVM) is developed to select efficient suppliers based on their predicted efficiency sco...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- ADS
دوره 2015 شماره
صفحات -
تاریخ انتشار 2015