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Journal ArticleDOI

A Fuzzy Matching based Image Classification System for Printed and Handwritten Text Documents

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TLDR
This article proposes a system that performs better than existing systems, and shows the results of experiments on this and other proposed systems.
Abstract
Thisarticleproposesabi-leveledimageclassificationsystemtoclassifyprintedandhandwritten Englishdocumentsintomutuallyexclusivepredefinedcategories.Theproposedsystemfollowsthe stepsofpreprocessing, segmentation, featureextraction, andSVMbasedcharacterclassification atlevel1,andwordassociationandfuzzymatchingbaseddocumentclassificationatlevel2.The systemarchitectureanditsmodularstructurediscussvarioustaskstagesandtheirfunctionalities. Further,acasestudyondocumentclassificationisdiscussedtoshowtheinternalscorecomputations ofwordsandkeywordswithfuzzymatching.Theexperimentsonproposedsystemillustratethatthe systemachievespromisingresultsinthetime-efficientmannerandachievesbetteraccuracywith lesscomputation timeforprinteddocuments thanhandwrittenones.Finally, theperformanceof theproposedsystemiscomparedwiththeexistingsystemsanditisobservedthatproposedsystem performsbetterthanmanyothersystems. KeywoRDS Confidence Computation, Document Image, Fuzzy Matching, Handwritten Documents, Performance Analysis, Printed Documents, SVM, Text Image Classification, Word Association

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Citations
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Journal ArticleDOI

Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data

TL;DR: In this paper, in-depth research and analysis is conducted on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion and the substring representation in the model ensures the generation of unregistered word vectors.

Applications of Text Detection and its Challenges: A Review

TL;DR: In this article, the authors present a survey of text detection and recognition from images to a large extent, including general documents such as newspapers, books and magazines, forms, scientific documents, maps, architectural and engineering drawings, and scene images with textual information.
Journal ArticleDOI

Advanced Applications on Bilingual Document Analysis and Processing Systems

TL;DR: A journey of bilingual NLP and image-based document classification systems is discussed and an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs is provided.
Journal ArticleDOI

Opinion Classification of Product Reviews Using Naïve Bayes, Logistic Regression and Sentiwordnet: Challenges and Survey

A Dadhich, +1 more
TL;DR: A detailed review and comparative analysis of various existing sentiment analysis algorithms especially for the Amazon products, which have worked upon the supervised learning techniques called Naïve Bayes, logistic regression and SentiWordNet are presented.
References
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Journal ArticleDOI

Hindi Text Document Classification System Using SVM and Fuzzy: A Survey

TL;DR: A new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic first pre-processes and then classifies textual imaged documents into predefined categories.
Proceedings ArticleDOI

Handwritten document retrieval strategies

TL;DR: Three techniques each exploring a different approach for solving the noisy text retrieval task using a novel bootstrapping mechanism to refine the OCR'ed text and uses the cleaned text for retrieval.
Proceedings ArticleDOI

Comparison of different classifiers for script identification from handwritten document

TL;DR: A series of classifiers namely Logistic Model Tree, Random Forest, Multi Layer Perceptron, Sequential Minimal Optimization, LibLINEAR, RBFNetwork and Fuzzy Unordered Rule Induction Algorithm are applied on the feature set to classify among the six handwritten scripts and the results are compared.
Proceedings ArticleDOI

An enhanced fuzzy similarity based concept mining model for text classification using feature clustering

TL;DR: A Fuzzy Similarity based Concept Mining Model Using Feature Clustering (FSCMM-FC) is proposed which capably categorizes various seen and known text documents into different predefined and mutually exclusive categories groups by keeping the data (or feature set dimension) very low.
Proceedings ArticleDOI

Classifying Textual Components of Bilingual Documents with Decision-Tree Support Vector Machines

TL;DR: A decision-tree support vector machine (DTSVM) method is employed, which decomposes a given data space into small regions and trains local SVMs on those regions and achieves higher than 99.6% test accuracy in classifying a textual component into Chinese, alphanumeric, and punctuation.