第七回全日本コンピュータビジョン勉強会 A Multiplexed Network for End-to-End, Multilingual OCR
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2021年の6月19日～25日にかけて開催されるコンピュータビジョンのトップカンファレンスの一つである 「Conference on Computer Vision and Pattern Recognition(CVPR) 2021」の論文読み会、その発表資料です。
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第七回全日本コンピュータビジョン勉強会 A Multiplexed Network for End-to-End, Multilingual OCR
- 2. ࣗݾհʢ͘ʂʣ Ԭຊେʢ͓͔ͱ·ͱʣ 5XJUUFS3PBESPMMFS@%46 ✓ ֶੜ࣌ژେֶͰը૾ೝࣝΛઐ߈ ✓ ΦϜϩϯʹͯɺࣄٕͱۀज़ͷೋྲྀਓࡐͱͯ͠#J[%FWd3%·Ͱखֻ͚Δ ✓ ΑΓɺ-*/ࣜגձࣾͷ$PNQVUFS7JTJPO-BC5FBNʹॴଐ˒/FX ➡ -*/$7-BCΛzӉҰָ͘͠ಇ͚ΔνʔϜzʹ͘͢νʔϜϏϧυத ➡ ເʮʹژಌΕΒΕΔڌڀݚΛ࡞Δʯ͜ͱ ˞ຊͷൃදҰൠެ։ใͷจΛհ͢ΔҐஔ͚Ͱ͢ɻ ˞ॴଐஂମؔ͋Γ·ͤΜɻ
- 3. հ͢Δจ ● .VMUJQMFYFE/FUXPSLGPSOEUPOE .VMUJMJOHVBM0$3 'BDFCPPL* IUUQTBSYJWPSHQEGQEG ✓ ޫֶจࣈೝࣝ 0$30QUJDBM$IBSBDUFS3FDPHOJUJPO ͷڀݚ ✓ ैདྷɺzΞϧϑΝϕοτzʹযΛ͕͋ͯͨڀݚଟ͔ͬͨ ✓ ଟޠݴରԠ͢Δʹͯ͠ɺग़ྗΫϥεΛे͔Βઍͱ૿͚ͩ͢ͷ߽շͳख๏͕ଟ͔ͬͨ ✓ հจɺΞϥϒޠຊܭͲͳޠछʹରԠͭͭ͠ɺεϚʔτͳଟޠݴϞσϧΛఏҊ
- 6. จհͷલʹɺஸೡʹݴ༿ͷఆٛΛཧ͍ͤͯͩ͘͞͞ 2VFTUJPOɿ0$3͕ݕग़͢Δͷ4FOUFODFʁ8PSEʁ$IBSBDUFSʁ w 4FOUFODFɹ➔ɹz*BNEPOFXJUINBOLJOE +0+0z w 8PSEɹɹɹ➔ɹz*zɺlBNzɺlEPOFzɺlXJUIzɺlNBOLJOEzɺl zɺl+0+0zɺlz w $IBSBDUFS➔ɹlNzɺBzɺlOzɺlLzɺlJzɺlOzɺlEz OTXFSɿͲΕͰͳ͍ w 0$3zҙຯతͳ୯ҐzͰͳ͘zߏతͳ୯ҐzͰจࣈྻΛݕग़͢ΔʢJOTUBODFMFWFMͱͿݺʣ w ҎޙɺຊࢿྉͰߏతʹ͋Δఔ·ͱ·ͬͨจࣈྻΛʮ5FYUςΩετʯͱͿݺ w จࣈͣͭʢ$IBSBDUFSʣΛରʹ͢ΔͱɺΞϊςʔγϣϯख๏͕ҟͳΔͷͰ໌֬ʹ۠ผ͢Δ
- 7. จհͷલʹɺͲΜͳΞϊςʔγϣϯΛ͏ͷ͔ཧ • 5FYUMBCFMɹˡԼهͷը૾தʹͳ͍͕ɺ͜ͷ߹ʮ#--:4ʯ͕5FYUMBCFMʹ૬͢Δɻ͜Ε͍͍ͩͨೖखͰ͖Δɻ • 5FYUMFWFMCPVOEJOHCPYʢ੨ʣ • 5FYUQPMZHPOBMCPVOEJOHCPYʢʣ • QPMZHPOBMCPVOEJOHCPYΛ࠷খۣͰܗғͬͨCPVOEJOHCPYʢʣˡ੨͕ͳ͍߹ʹ༻׆ • $IBSBDUFSMFWFMCPVOEJOHCPYʢԫʣˡ΄΅ଘࡏ͠ͳ͍ɺ΄ͱΜͲͷ߹σʔλˍࢣڭϥϕϧΛը૾߹Ͱੜ͢Δɻ • $IBSBDUFSMBCFM ԫ৭ృΓͷจࣈ ˡ΄΅ଘࡏ͠ͳ͍ɺ΄ͱΜͲͷ߹σʔλˍࢣڭϥϕϧΛը૾߹Ͱੜ͢Δɻ
- 9. Text Detectionͱʁ ը૾ʹ·ؚΕΔશͯͷ5FYUྖҬΛɺۣܗ1PMZHPOͰநग़͢Δ w 'BTU3$//ͳͲطଘͷ%FUFDUJPOϞσϧΛར༻Ͱ͖Δ w ςΩετ͕ࣼΊʹͳΔͱඞཁҎ্ʹۣ͕ܗେ͖͘ͳΔ w ςΩετྖҬҎ֎ͷಛྔࠞೖ͢Δ߹͕ଟ͍ w Ξϊςʔγϣϯʹख͕͔͔ؒΔ w ϚεΫॲཧͳͲͷՃʹΑͬͯςΩετྖҬ͔ΒͷΈ ಛྔΛநग़Մೳ ྫɿ'BTU3$//ͷΑ͏ʹ4MJEJOH8JOEPXͰݕग़͢Δ ྫɿ5FYU/055FYUͷ4FHNFOUBUJPOΛ͢Δ
- 10. Text Recognitionͱʁ ݕग़ͨ͠5FYUྖҬʹ·ؚΕΔ$IBSBDUFSྻΛೝࣝ͢Δ -07 w ֤$IBSBDUFSΫϥε
- 11. എܠΫϥεͰ4FHNFOUBUJPO w մײΛͬͯલͱܠ༧ଌ͞ΕͨྖҬʹจࣈ͕͋ΔͱԾఆ͢Δ w Ͳͷ$IBSBDUFSΫϥεʹଐ͢Δ͔શϐΫηϧͰଟܾ͢Δ w $IBSBDUFSMFWFMͷΞϊςʔγϣϯ͕ඞཁͳͷͰେ w จࣈͷॱং͕֫ಘͰ͖ͳ͍՝ w 5FYUྖҬͷಛྔΛྻܥσʔλͷʹܗม͢Δ w 3//ϕʔεͷϞδϡʔϧͰਪʢ-45.(36ʣ w 4QBUJBMUUFOUJPOߏΛೖΕΔ߹ଟ͍ w $IBSBDUFSMFWFMͷΞϊςʔγϣϯෆཁͳͷͰ͓खܰ ྫɿ4FNBOUJD4FHNFOUBUJPOͰ$IBSBDUFSΛ༧ଌ͢Δ ྫɿ4FR4FROD%FDͰจࣈͣͭग़ྗ͢Δ
- 13. จհͷલʹɺ0$3ͷ͠͞Λྻڍ • ςΩετͷํ͕λςɺϤίɺφφϝɺΧʔϒͳͲ༷ʑͰ͋Δ • ̍ຕͷը૾ͰෳͷςΩετํ͕ࠞࡏ͢Δ • ̍ͭͷςΩετ͕̎ͭҎ্ʹɺ·ͨɺҟͳΔ̎ͭҎ্ͷςΩετ͕ͭʹ݁߹ͯ͠ݕग़ͯ͠͠·͏ • Ξϊςʔγϣϯ͕ݶΒΕΔɻಛʹ$IBSBDUFSMFWFMͷΞϊςʔγϣϯ΄΅खʹೖΒͳ͍ • ಛྔநग़ɺ%FUFDUJPOɺ3FDPHOJUJPOͱ͍ͬͨෳͷҟͳΔػೳϞδϡʔϧΛͰֶश͍ͨ͠ ʢֶश͕ޮతͩ͠ɺࣗવͳઃͳʹܭΔ͠ɺҟͳΔػೳϞδϡʔϧಉ࢜ͷ૬ظ͕ิޓͰ͖ΔͨΊʣ
- 15. 0$3ͰΑ͘Ҿ༻͞ΕΔख๏ .-5 • $[FDI5FDIOJDBM6OJWFSTJUZ $BSOFHJF.FMMPO6OJWFSTJUZ • .-5BO6ODPOTUSBJOFEOEUPOE.FUIPEGPS.VMUJ-BOHVBHF4DFOF5FYU • IUUQTBSYJWPSHQEGQEG $IBS/FU • .BMPOH5FDIOPMPHJFT • $POWPMVUJPOBM$IBSBDUFS/FUXPSLT • IUUQTBSYJWPSHQEGQEG 5FYU4QPUUFS • 'BDFCPPL* )VB[IPOH6OJWFSTJUZ • .BTL5FYU4QPUUFSW4FHNFOUBUJPO1SPQPTBM/FUXPSLGPS3PCVTU4DFOF5FYU4QPUUJOH • IUUQTBSYJWPSHQEGQEG $3'54 • $MPWB*3FTFBSDI /73$PSQ • $IBSBDUFS3FHJPOUUFOUJPO'PS5FYU4QPUUJOH • IUUQTBSYJWPSHQEGQEG
- 16. .-5 $$7` CRAFTS ը૾શମͷ ಛྔநग़ ResNet34Λϕʔεͱͨ͠FPN(Feature Pyramid Net)ɻ ςΩετ ྖҬݕग़ 1/4 Scaleͷ֤࠲ඪͰ Text/NOT Textɺb-boxɺAngleΛਪɻ ʢAncher༻͠ͳ͍ʣ ςΩετ ྖҬͷ ಛྔநग़ ݕग़ͨ͠b-box͔ΒճసΈܰݮΛ తʹɺύϥϝλਪఆ͠ͳ͕Β spatial transformer layerΛద༻͢Δɻ ςΩετ ೝࣝ ConvͰจࣈೝࣝثΛߏ ೖྗɿԣ෯͚ͩՄมͷಛྔ ग़ྗɿจࣈ(7500)ͷlog-softmax ग़ྗ͢Δจࣈಛྔͷԣ෯ʹൺྫ ͯ͠૿͢ɻ ֶश ը૾߹ʹΑͬͯଟޠݴͷֶशσʔλ Λߏஙɻ ˛ఏҊϞσϧͷશମ૾ɻݕग़෦ͱೝࣝ෦͕ྻʹฒͿɻ ˛ֶश༻ͷ߹ը૾
- 17. $IBS/FU $713` ˛.BTL5FYU4QPUUFS$3'54ʹൺΔͱɺ ɹςΩετจࣈͷೝࣝॲཧ͕ฒྻͰҰ͞ͳʹؾΕΔɻ CharNet ը૾શମͷ ಛྔநग़ ResNet-50 ͱ Hourglass networks(Newell 2016)ͷ Έ߹Θͤɻ ςΩετ ྖҬݕग़ Text Detection BrunchͱCharacter BrunchΛฒྻʹઃ͚Δɻ ɾText Detection Brunch ࣼΊΧʔϒʹରԠՄೳͳ طଘख๏(EAST, Textfield)Λద༻ɻ ςΩετྖҬΛग़ྗ͢Δɻ ɾCharacter Brunch 3ͭͷϞδϡʔϧΛฒྻʹஔ -(1)[Text/NOT text] ͷsegmentation -(2)b-boxʹΑΔCharacter Detection -(3)จࣈͷଟΫϥεsegmentation Characterͷb-boxͱϥϕϧΛग़ྗ͢ Δɻ ݕग़ͨ͠ςΩετྖҬʹ·ؚΕΔจ ࣈू߹Λग़ྗ͢Δ (ͳͷͰɺग़ྗ ʹີݫςΩετͰͳ͍)ɻ ςΩετ ྖҬͷ ಛྔநग़ ςΩετ ೝࣝ ֶश ֶशʹText-levelͱcharacter-level྆ ํͷΞϊςʔγϣϯ͕ඞཁͳͷͰɺ ߹σʔλͰֶश͢Δɻ ͦͷޙɺ࣮σʔλΛͬͯ Weakly Supervised Learning͢Δɻ ˝5FYU%FUFDUJPO#SVODI ɹςΩετྖҬΛݕग़ ˝$IBSBDUFS#SVODI෦ͰͭͷॲཧΛฒྻ࣮ߦͯ͠ɺ ɹ$IBSBDUFSMFWFMͷݕग़ͱೝࣝΛ࣮ߦ͢Δɻ
- 18. 5FYU4QPUUFSW $$7` ˛ςΩετྖҬΛ4ISJOLͨ͠ྖҬΛࢣڭσʔλͱֶͯ͠शͤ͞Δɻ ͜ΕʹΑΓྡͷςΩετ͕ͬͭ͘͘ͷΛ͙ʢ࣍ͷॲཧʹҠΔͱ͖ʹVOTISJOL͢ΕΑ͍ʣ ◀︎ ˛PSͰಛྔΛϚεΫ͕͚͢Δ ˝௨Γͷํ๏ͰจࣈྻΛ֫ಘ͢ΔɻԼஈͷख๏4.ͰจࣈϨϕϧͷΞϊςʔγϣϯෆཁ TextSpotter (v1~v3) ը૾શମͷ ಛྔநग़ ResNet50ΛϕʔεʹFPNΛઃ͚Δ(v2) ResNet50ΛϕʔεʹU-NetΛઃ͚Δ(v3) ςΩετ ྖҬݕग़ Fast-RCNNϕʔεͷAncherʹΑΔݕग़ (v2) Text/NOT TextΛSegmentation(v3) ςΩετ ྖҬͷ ಛྔநग़ AncherͰݕग़ۣͨ͠ྖܗҬʹRoI AlignΛద ༻ͯ͠ಛྔநग़ (v2) Segmentation݁ՌΛ࠷খۣͰܗΓग़͠ɺ ಛྔʹRoI AlignͱMaskΛద༻(v3) ςΩετ ೝࣝ (1)֤จࣈʴഎܠͷSegmentationΛ࣮ߦɻ จࣈީิྖҬͰଟܾ(PixelVoting)Λͯ͠ จࣈΛఆɻ (2)Sequentialͳಛྔʹมͯ͠Attention ͖ͷseq2seqͰจࣈྻΛग़ྗɻ (1)(2)ͷ2ͭͷ༧ଌ݁ՌΛ֫ಘޙɺ৴པͷ ߴ͍ํΛ࠾༻͢Δɻ ֶश Character-levelͷΞϊςʔγϣϯ͕ͳͯ͘ (2)ֶशՄೳʢ˞(1)ͷֶशʹඞཁʣɻ
- 20. ͓ͨͤ͠·ͨ͠ɺͬͱจհʹҠΓ·͢ɻ ● .VMUJQMFYFE/FUXPSLGPSOEUPOE .VMUJMJOHVBM0$3 'BDFCPPL*ɹIUUQTBSYJWPSHQEGQEG
- 24. ΞΠσΞɿMulti-HeadʹΑΔଟޠݴରԠ ՝ɿଟޠݴରԠ͍ͨ͠ʂ͔֦͠ு༰қʹ͍ͨ͠ʂ ➡ ୯७ͳํ๏ɺؚ͕̔ޠݴΉશͯͷจࣈΛѻ͑ΔΑ͏ग़ྗΫϥεΛ֦ு͢Δ͜ͱ ➡ ͨͩ͠ɺຊޠɾதࠃޠɾޠࠃؖΛѻ͓͏ͱ͢Δͱສఔ·ͰΫϥε͕૿Ճ͢Δ ➡ ·ͯ͠ɺʹޠݴΑͬͯܗɾॎॻ͖ɾԣॻ͖ͳͲಛ͕େ͖͘ҟͳΔ ➡ ͜ΕΒΛͭͷೝࣝثʢ4JOHMF)FBEʣͰѻ͏ͷదͩΖ͏͔ʁ ➡ จࣈͰՃআͨ͘͠ͳͬͨ߹ɺ࠷ॳ͔Βֶश͢Δख͕ؒൃੜ͔͠Ͷͳ͍ ΞΠσΞ ➡ .VMUJIFBEߏΛ࠾༻ɻͭͭͷʹޠݴಛԽͨͭ͠ܭͷจࣈೝࣝثΛஔͨ͠ɻ ➡ ͋Θͤͯɺޠݴೝࣝثʢ-BOHVBHF1SFEJDUJPO/FUXPSL -1/ ʣΛஔͨ͠ɻ ➡ ςΩετྖҬ͝ͱʹޠݴೝࣝΛͯ͠ɺ࠷దͳจࣈೝࣝثΛͭબΜͰਪ࣮ࢪͨ͠ɻ ςΩετྖҬ͝ͱʹ จࣈೝࣝثΛ͍͚ ࣝޠݴผ݁ՌʹԠͯ͡ จࣈೝࣝثΛΓସ͑ ෳͷจࣈೝࣝث Λஔͨ͠
- 25. ΞΠσΞɿLanguageͷࢣڭσʔλΛඞཁͱ͠ͳֶ͍श ՝ɿޠݴೝࣝثΛઃஔͨ͠ͷͷɺޠݴͷΞϊςʔγϣϯ͕গͳֶͯ͘शͰ͖ͳ͍ɻ ➡ ޠݴͷΞϊςʔγϣϯ͕͋ΕɺͦΕΛ༧ଌͰ͖ΔΑ͏ʹޠݴೝࣝثΛֶशͤ͞Εྑ͍ɻ ➡ ͔͠͠ɺ࣮ࡍΞϊςʔγϣϯ͕΄ͱΜͲͳ͍ɻͦΕʹɺϞδϡʔϧݸผͰͳ͘ʹֶश͍ͨ͠ɻ ΞΠσΞ ➡ ޠݴΞϊςʔγϣϯ͕ແͯ͘ɺςΩετϥϕϧͷΈͰʹֶशͰ͖Δํ๏ΛߟҊɻ ➡ จࣈೝࣝʹثඇରԠͷจࣈʢଞޠݴͷจࣈʣ͕ೖྗ͞Εͨ߹ʹϖφϧςΟΛ͔͚ͨɻ DUɿU൪ͷจࣈ ZUɿU൪ͷจࣈͷࢣڭσʔλ $Sɿจࣈೝ͕ࣝػѻ͏จࣈू߹ ̞ɿPSͷΛฦ͢ 5ɿ࠷େग़ྗจࣈʢݻఆύϥϝλʣ ޠݴೝࣝޠݴʹثΛਪͤͯ͞ɺ DSPTTFOUSPQZMPTTͰֶश͢Δɻ 5FYUMBCFM͚ͩͰֶशͤ͞Δɻ จࣈೝࣝʹػඇରԠͷจࣈ͕ೖྗ͞ΕΔͱϖφϧςΟЌΛ͔͚Δɻ ͜ΕʹΑΓɺޠݴೝ͕ࣝثదͳจࣈೝࣝثΛબ͢ΔΑ͏ֶश͢Δɻ Mɹɹɿޠݴ Q M ɿϞσϧ͕ਪͨ֬͠ MMBOHɿѻ͏ޠݴͷ૯ ޠݴΞϊςʔγϣϯ͕͋Δ߹ ޠݴΞϊςʔγϣϯ͕ͳ͍߹
- 26. ࣮݁ݧՌͱߟ
- 27. ଟޠݴͷText Spottingͷ࣮݁ݧՌˠʮCRAFTSҎ֎ʹউͬͨͥʂʯ CRAFTS(paper ) CRAFTͷจ͕ใࠂ͞Ε͍ͯΔ࣮݁ݧՌ CRAFT S ஶऀΒ͕CRAFTΛ࠶࣮ͯ͠ݱධՁͨ݁͠Ռ Single-head TextSpotte r ఏҊख๏ΛMulti-HeadʹͤͣSingle-HeadͰ8ޠݴͷશͯͷจࣈ(9000छ)ʹରԠͤͨ͞Ϟσϧ Multiplexed TextSpotte r ຊࢿྉͰհ͍ͯ͠ΔఏҊख๏ɻ ैདྷͷଟޠݴରԠOCRϞσϧͱɺ8ޠݴͷText SpottingλεΫͰੑೳൺֱͨ͠ ※࣮ݧσʔλʹଟޠݴΛؚΉ”MTL19 Dataset”Λར༻ Ὂ݁Ռɿ֓Ͷߴੑೳͱͳ͕ͬͨɺ།ҰɺCRAFTʹಧ͔ͣ F Precision Recall
- 28. ଟޠݴͷText Detectionͷ࣮݁ݧՌˠʮCRAFTS(paper)Ҏ֎ʹউͬͨͥʂʯ ଟޠݴσʔλʢMLT19ʣΛରʹςΩετݕग़Ͱੑೳൺֱͨ͠ Ὂ݁Ռɿ֓Ͷߴੑೳͱͳ͕ͬͨɺ།ҰɺCRAFT(paper)ʹಧ͔ͣ Average Precision F Precision Recall CRAFTS(paper ) CRAFTͷจ͕ใࠂ͞Ε͍ͯΔ࣮݁ݧՌ CRAFT S ஶऀΒ͕CRAFTΛ࠶࣮ͯ͠ݱධՁͨ݁͠Ռ Single-head TextSpotte r ఏҊख๏ΛMulti-HeadʹͤͣSingle-HeadͰ8ޠݴͷશͯͷจࣈ(9000छ)ʹରԠͤͨ͞Ϟσϧ Multiplexed TextSpotte r ຊࢿྉͰհ͍ͯ͠ΔఏҊख๏ɻ
- 30. ͦͦͷతϋϯυϦϯά͍͢͠ଟޠݴϞσϧΛఏҊ͢Δ͜ͱ ✓ ఏҊϞσϧMulti-HeadͳͷͰޠݴͷՃআ͕༰қ👍 ✓ 10000ΫϥεͷSoftmaxΑΓඒ͍͠ߏͩͱ͑ݴΔ👍 ✓ ͪΌΜͱଞͷOCRϞσϧͱಉ༷ʹE2EʹֶशՄೳ👍 CRAFTSͷੑೳʹಧ͔ͳ͔͕ͬͨվྑͷ༨͕·ͩ·ͩ͋Δ ✓ CRAFTSLink ScoreͷಋೖʹΑͬͯॎॻ͖ςΩετʹ͍ڧ ✓ ॎॻ͖ςΩετʹର͢ΔೝࣝੑೳࠩͰউෛ͕͔Εͨͱߟ͍ͯ͠Δ ✓ CRAFTSͷఏҊख๏ʹಋೖՄೳʢͦΕΛ࣮ͨ͠TextSpotter v4͕ۙʑൃද͞ΕͨΓͯ͠…!?ʣ ✓ ଞʹɺจࣈೝࣝثͷύϥϝλɺࣄલֶशσʔλྔ͕ɺCRAFTͷํ͕ང͔ʹଟ͍ɺͳͲͳͲɺɺɺ
- 31. ·ͱΊɿଟޠݴରԠͷ0$3Λֶश͍͢͠ˍվ͍͢͠ߏͰ࣮ͨ͠ݱʂ ՝ɿଟޠݴରԠ͍ͨ͠ʂ͔֦͠ு༰қʹ͍ͨ͠ʂ ➡ .VMUJ)FBEߏΛ࠾༻ͯ͠ɺͭͭͷʹޠݴಛԽͨͭ͠ܭͷจࣈೝࣝثΛஔͨ͠ ➡ ޠݴೝࣝثΛઃஔͯ͠ɺೖྗςΩετʹର͢Δޠݴਪ݁ՌʹԠͯ͡จࣈೝࣝثΛΓସ͑ͨɻ ՝ɿޠݴೝࣝثΛઃஔͨ͠ͷͷɺޠݴͷΞϊςʔγϣϯ͕গͳֶͯ͘शͰ͖ͳ͍ɻ ➡ จࣈೝࣝʹثඇରԠͷจࣈʢଞޠݴͷจࣈʣ͕ೖྗ͞Εͨ߹ʹϖφϧςΟΛ͔͚ͨɻ ➡ ͜ΕʹΑΓɺޠݴΞϊςʔγϣϯ͕ແͯ͘ɺςΩετϥϕϧͷΈͰʢޠݴೝࣝثؚΊͯʣʹֶशͨ͠ɻ ࣮݁ݧՌɿ ● 4P5ʹಧ͔ͳ͔ͬͨͷͷɺ5FYU%FUFDUJPOͱ5FYU3FDPHOJUJPOͰैདྷख๏ͱಉఔͷߴੑೳΛୡɻ
- 33. ิɿσʔληοτ Ex. Language Script Data Difficulty Annotation ICDAR 2017 MLT dataset (MLT17) 9 languages representing 6 different scripts equally multi-oriented scene text annotated using quadrangle bounding boxes. ICDAR 2019 MLT dataset (MLT19) 10 languages representing 7 different scripts. multi-oriented scene text annotated using quadrangle bounding boxes. Total-Text dataset English language. wide variety of horizontal, multi-oriented and curved text annotated at word-level using polygon bounding boxes. ICDAR 2019 ArT dataset (ArT19) English and Chinese languages highly challenging arbitrarily shaped text annotated using arbitrary number of polygon vertices ICDAR 2017 RCTW dataset (RCTW17) Chinese scene text in Chinese drawing polygons to surround every text line ICDAR 2019 LSVT dataset (LSVT19) Chinese, but also has about 20% of its labels in English words. street view text in Chinese drawing polygons to surround every text line ICDAR 2013 dataset (IC13) English language horizontal text annotated at word-level using rectangular bounding boxes ICDAR 2015 dataset (IC15) English language multi-oriented scene text annotated at word-level using quadrangle bounding boxes. $IBSBDUFSMFWFMͷOOPUBUJPO͕ແ͍ɺ͕ޠݴภ͍ͬͯΔʹɻ