Towards End-To-End Text Spotting With Convolutional Recurrent Neural Networks

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1、TowardsEnd-to-endTextSpottingwithConvolutionalRecurrentNeuralNetworksHuiLi1∗,PengWang2,1∗,ChunhuaShen11TheUniversityofAdelaide,andAustralianCentreforRoboticVision2NorthwesternPolytechnicalUniversity,Chinae-mail:{hui.li02,chunhua.shen}@adelaide.edu.au,peng.wang@nwpu.edu.

2、cnAbstractregions.Thenwordrecognitionisperformedoncroppedboundingboxesusingdifferentapproaches,followedbyInthiswork,wejointlyaddresstheproblemoftextde-wordseparationorcharactergrouping.Anumberoftech-tectionandrecognitioninnaturalsceneimagesbasedonniquesarealsodevelopedw

3、hichsolelyfocusontextdetec-convolutionalrecurrentneuralnetworks.Weproposeationorwordrecognition.However,thetasksofworddetec-unifiednetworkthatsimultaneouslylocalizesandrecog-tionandrecognitionarehighlycorrelated.Firstly,thefea-nizestextwithasingleforwardpass,avoidinginte

4、rmediatetureinformationcanbesharedbetweenthem.Inaddition,processes,suchasimagecropping,featurere-calculation,thesetwotaskscancomplementeachother:detectingtextwordseparation,andcharactergrouping.Incontrasttoex-regionsaccuratelyhelpsimproverecognitionperformance,istingapp

5、roachesthatconsidertextdetectionandrecogni-andrecognitionoutputscanbeusedtorefinedetectionre-tionastwodistincttasksandtacklethemonebyone,thesults.proposedframeworksettlesthesetwotasksconcurrently.Tothisend,weproposeanend-to-endtrainabletextspot-Thewholeframeworkcanbetrai

6、nedend-to-end,requir-ter,whichjointlydetectsandrecognizeswordsinnaturalingonlyimages,ground-truthboundingboxesandtextla-sceneimages.Anoverviewofthenetworkarchitectureisbels.TheconvolutionalfeaturesarecalculatedonlyoncepresentedinFigure1.Itconsistsofseveralconvolutionala

7、ndsharedbybothdetectionandrecognition,whichsaveslayers,aregionproposalnetworktailoredspecificallyforprocessingtime.Throughmulti-tasktraining,thelearnedtext(refertoasTextProposalNetwork,TPN),aRecurrentfeaturesbecomemoreinformativeandimprovestheoverallNeuralNetwork(RNN)enc

8、oderforembeddingproposalsperformance.Ourproposedmethodhasachievedcompeti-ofvaryingsizestofixed-lengthvectors,mu