Applications of Text Detection and its Challenges

Abstract

The rising need for automation of systems has effected the development of text detection and recognition from images to a large extent. Text recognition has a wide range of applications, each with scenario dependent challenges and complications. How can these challenges be mitigated? What image processing techniques can be applied to make the text in the image machine readable? How can text be localized and separated from non textual information? How can the text image be converted to digital text format? This paper attempts to answer these questions in chosen scenarios. The types of document images that we have surveyed include general documents such as newspapers, books and magazines, forms, scientific documents, unconstrained documents such as maps, architectural and engineering drawings, and scene images with textual information.

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

“…continued on following page challenges,solutions,andconstraints.Further,theydiscussedcontentforms,imageminingmethods, andlanguage/scriptidentificationandclassificationschemes,whichwerecomparedforcriteriaof languagesused,scriptandlanguagedetection,featureextraction,grayscaleorcoloredimage,font variation,resolution,printerorscannertype,andclassifier.Theirobservationsfoundthemaximum contributionintextualcontentformwithmonoandmulti-lingualdocumentsalongwiththescript identification,anduseof300DPI,grayscale,andSVMclassifier.Pal(2014)discussedareview onlanguageandscriptidentificationmethodswithfont-cum-stylerecognitionandfurtherprovided languageoverview,origin,difficulties,singleandmulti-scriptidentificationtechniquesforprintedand handwrittendocuments,challenges,andfinally,fontstyle,generation,variation,andtheirrecognition methods Nevetha and Baskar (2015). demonstrated text detection applications and techniques withtheirchallengesongeneral,scientific,unconstrainedandscenedocumentimagesoftextual information.Theyfurtherdiscussedtextrecognitionphasesofpreprocessing,segmentation,feature extraction,andrecognition.Felhi,TabboneandSegovia(2014)providedamulti-scalestroke-based pagesegmentationapproachtogetthetext,lines,photosandbackground.Theyfollowedthesteps ofglobalstrokewidthvariation-basedtextandlineCCdetection,imagesegmentationintophoto andbackgroundregionswithactivecontourmodel,textclassification,lineseparation,textcandidate clustering by mean-shift analysis, and finally, horizontal and vertical text regions separation word Recognition and Spotting Thissectionillustratesvariouswordrecognitionandspottingmethods.Thescalable,statistical,script independentline-basedwordspottingmethodperformedminimumpreprocessing,nosegmentation, fillermodelcreationinnon-keywordregions,featureextraction,and,finally,HiddenMarkovModel (HMM)basedrecognition(Wshah,KumarandGovindaraju,2014).Thismethodhasbeentested onEnglishdocumentsfromIAMdatasets,ArabicdocumentsfromAMAdatasets,andDevanagari documentsfromLAWdatasetsandfoundsystemcomplexityofO(K 2 L)+(R 2 L*)usinglexicon-…”