DjVu Kple Eƒe Kadodo Kple Nusɔsrɔ̃ Deto (2023): Nusiwo Wòle Be Nànya

DjVu nye nuŋlɔɖi ƒe ɖoɖo si woƒo ƒu si wowɔ gbã na nuŋlɔɖi siwo woɖe ɖe scan kple dijitaal nudzraɖoƒewo, eye eƒe kadodo kple nusɔsrɔ̃ deto va do abe tsatsam siwo ŋu kakaɖedzi le wu dometɔ ɖeka le egbegbe nuŋlɔɖiwo ƒe dɔwɔwɔ si AI ʋãna me. Esi mɔ̃wo sɔsrɔ̃ ƒe mɔnuwo le ŋgɔ yim la, DjVu ƒe xɔtuɖaŋu kple nuŋɔŋlɔ ƒe mɔnuwo va zu hehenaƒe kple dɔwɔwɔ ƒe taɖodzinu veviwo na ahɔhɔ̃mekawo ƒe kadodo ƒe ɖoɖo siwo kpɔa nuŋlɔɖi gãwo ƒe dijitaal zazã gbɔ.

Nuka Tututue Nye DjVu Kple Nukatae Wòle Vevie le AI ƒe Ɣeyiɣia Me?

| Nɔnɔmea zãa layered compression mɔnu si ma nuŋlɔɖi aɖe ɖe ŋgɔgbe (nuŋɔŋlɔ, fli ƒe nɔnɔmetata), megbe (amadede ƒe nɔnɔmetata), kple mask (nɔnɔme ŋuti nyatakakawo) ƒuƒoƒo me. Woƒoa ƒuƒoƒo ɖesiaɖe nu ƒu le eɖokui si to akɔntabubumɔnu tɔxɛwo zazã me.

Nusi na DjVu sɔ ŋutɔ egbea enye be ƒuƒoƒo geɖe ƒe gbegblẽ sia nyea ahuhɔ̃e na ɖoɖo ƒe nɔnɔme ɖeɖe si ɖea nusɔsrɔ̃ goglo ƒe xɔtuɖaŋuwo. Le kpɔɖeŋu me, convolutional neural networks (CNNs) wɔa nɔnɔmetatawo ŋudɔ to dzesidede nugbɔwo, emegbe nɔnɔmewo, emegbe xɔtuɖoɖo siwo le ɖoƒe kɔkɔwo me — ŋgɔyiyi si sɔ kple alesi DjVu ma nuŋlɔɖiwo ɖe nukpɔkpɔ gbãtɔwo me wòwɔ nuku ŋutɔ. Menye sukudede koe xɔtuɖoɖo ƒe sɔsɔ sia nye o; eƒe gɔmesese ŋutɔŋutɔwo le alesi wona hehe AI ɖoɖowo be woaxlẽ, aɖo wo ɖe hatsotsowo me, eye woaɖe gɔmesese tso ŋutinya me nuŋlɔɖiwo me.

Aleke Wole Hehe nam Nusɔsrɔ̃ Deto ƒe Kpɔɖeŋuwo le DjVu Nuŋlɔɖiwo ƒe Nudzraɖoƒewo?

Agbalẽdzraɖoƒe gãwo — siwo dome Internet Nudzraɖoƒe, si me DjVu faɛl miliɔn geɖe le hã le — va zu sikakuƒewo hena hehenana optical character recognition (OCR) kple nuŋlɔɖi gɔmesese ƒe kpɔɖeŋuwo. Nusɔsrɔ̃ goglo ŋuti numekulawo zãa DjVu ƒe nudzraɖoƒewo elabena ɖoɖoa kpɔa agbalẽtata ƒe nyatakaka nyuiwo ta le nutete ƒe xexlẽme si gbɔ eme gɔ̃ hã me, si wɔe be wònyo wu JPEG scan siwo bu na nusɔsrɔ̃dɔ siwo dzi wokpɔna.

Wotrɔ asi le egbegbe transformer-based models abe LayoutLM kple DocFormer ŋu nyuie le datasets siwo me DjVu-sourced content le la dzi. Kpɔɖeŋu siawo srɔ̃a alesi woatsɔ teƒe ƒe ɖoɖo asɔ kple gɔmesese ƒe gɔmesese — gɔmesese be tanya dzẽ ɖea vevienyenye fiana alo be sɔti ƒe gbagbã fiaa akpa ƒe tɔtrɔ. DjVu ƒe ƒuƒoƒo dzadzɛ ƒe mama na anyigba-nyateƒe ƒe nuŋlɔɖi le bɔbɔe ŋutɔ, si ɖea dzesidede ƒe gazazã si ɖea fu na kɔmpiuta ƒe nukpɔkpɔ hehenamɔ̃ geɖewo dzi kpɔtɔna.

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Nukae Nye DjVu-Informed Deep Learning Systems ƒe Dɔwɔwɔ Nyuiwo?

DjVu ƒe nudzraɖoƒewo tsɔtsɔ ƒo ƒu kple nusɔsrɔ̃ deto ƒe ŋusẽkpɔɖeamedzi ŋutɔŋutɔ le sesem le dɔwɔƒe geɖewo me xoxo. Dɔwɔwɔ veviwo dometɔ aɖewoe nye:

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• Ŋutinya me nuŋlɔɖiwo ƒe dijitaal zazã: Dɔwɔƒewo abe dukɔa ƒe agbalẽdzraɖoƒewo kple sukuwo ƒe nudzraɖoƒewo le AI si DjVu na hehee zãm tsɔ le asinuŋɔŋlɔgbalẽ siwo woŋlɔ kple asi, senyawo ŋuti nuŋlɔɖiwo, kple nuŋlɔɖi siwo mebɔ o siwo axɔ ƒe bla nanewo hafi woate ŋu awɔ dɔ tso wo ŋu kple asi la le wo ɖokui si.
• Se kple sedziwɔwɔ ŋuti nuŋlɔɖiwo me dzodzro: Senyawo gbɔ kpɔƒewo kple gadzikpɔƒewo zãa kpɔɖeŋu siwo wona hehee le nubabla ƒe agbalẽdzraɖoƒe siwo tso DjVu gbɔ be woaɖe nyagbewo ɖa, ade dzesi afɔkugbe, eye woade dzesi ɖoɖowɔwɔ ƒe nyawo le agbɔsɔsɔ me.
• Atikewɔwɔ ŋuti nuŋlɔɖiwo wɔwɔ: Lãmesẽnyawo gbɔ kpɔkpɔ ƒe ɖoɖowo le dɔnɔwo ƒe faɛl xoxo siwo wodzra ɖo ɖe DjVu ƒe nɔnɔme me trɔm ɖe elektrɔnik lãmesẽ ŋuti nuŋlɔɖi siwo woɖo ɖe ɖoɖo nu, siwo woate ŋu adi la me to AI pɔmpi siwo léa dɔléle ŋuti nuŋlɔɖiwo kple nuŋlɔɖi siwo woŋlɔ kple asi la zazã me.
• Sukunyawo me numekuku ƒe ablaɖeɖe: Dzɔdzɔmeŋutinunyalawo zãa nusɔsrɔ̃ goglo ƒe ɖoɖo siwo wona hehee le dzɔdzɔmeŋutinunya ƒe magazinewo ƒe nudzraɖoƒewo (woma wo dometɔ geɖe abe DjVu ene) tsɔ wɔa agbalẽwo me toto gãwo, nyayɔyɔ ƒe kadodo me dzodzro, kple nukpɔsusuwo dzidzi.
• Tata kple nyatakakawo dzikpɔkpɔ: Nyadzɔdzɔdɔwɔƒewo wɔa metadata ƒe dzesidede, gomenɔamesiwo dzikpɔkpɔ, kple nyatakakawo gbugbɔgaɖoanyi le wo ɖokui si to woƒe DjVu nudzraɖoƒe agbalẽdzraɖoƒewo ŋudɔwɔwɔ to nuŋlɔɖiwo gɔmesese ƒe kpɔɖeŋuwo dzi.

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Kuxi Kawoe Nusɔsrɔ̃ Deto Doa Go Ne Wole DjVu Faɛlwo Ŋu Dɔ wɔm?

Togbɔ be nuwɔwɔ aduadu si ŋugbe wodo hã la, mɔ̃ɖaŋununya ƒe mɔxenu veviwo gakpɔtɔ li. DjVu ƒe proprietary compression codec fia be raw neural networks mateŋu awɔ dɔ tso format la ŋu le dzɔdzɔme nu o — ele be woaɖe nuŋlɔɖiwo gɔme gbã eye woawɔ rasterized hafi woatsɔ wo ana ɖe standard image-based models me. Decoding afɔɖeɖe sia to preprocessing latency kple potential quality degradation vɛ ne womewɔ parameters nyuie o.

Gakpe ɖe eŋu la, ƒuƒoƒo geɖe ƒe wɔwɔme si na DjVu wɔa dɔ nyuie na amegbetɔ nuxlẽlawo alea la nye kuxi na nusɔsrɔ̃ goglo ƒe mɔ̃ siwo tso nuwuwu vaseɖe nuwuwu. Nukpɔkpɔ ƒe tɔtrɔ akpa gãtɔ kpɔa mɔ na nɔnɔmetata ƒe tensor ɖeka si wɔ ɖeka; nuɖuɖu nana ŋgɔgbe kple megbe ƒuƒoƒoawo ɖe vovo bia be woawɔ xɔtuɖaŋu siwo wowɔ ɖe ɖoɖo nu alo fusion ƒuƒoƒo siwo naa kpɔɖeŋua ƒe sesẽ kpena ɖe eŋu. Numekulawo le ŋku lém ɖe susu ƒe mɔnu siwo ateŋu awɔ dɔ le dzɔdzɔme nu le DjVu ƒe nɔnɔmetata siwo gblẽ dzi, si aʋu dɔwɔwɔ nyuie ƒe viɖe veviwo le nuŋlɔɖiwo wɔwɔ ƒe dɔwɔwɔ gãwo me.

Nukae Etsɔme Ahe Na DjVu kple Neural Document Processing?

| Gbegbɔgblɔ gã ƒe kpɔɖeŋu siwo me wozãa mɔnu vovovowo le siwo ate ŋu awɔ dɔ tso nuŋɔŋlɔ, ɖoɖo, kple nɔnɔmetata me nyawo ŋu le ɣeyiɣi ɖeka me la le nuŋlɔɖiwo gɔmesese bum xoxo abe dɔ si wɔ ɖeka ene tsɔ wu be wòanye afɔɖeɖe vovovowo ƒe mɔ̃.

Retrieval-augmented generation (RAG) systems ƒe dzidziɖedzi hã tsɔ DjVu ƒe nudzraɖoƒewo ɖo nɔƒe si nye sidzedze veviwo. Habɔbɔ siwo dea ga fifia ɖe woƒe DjVu nuƒoƒoƒuwo ƒe tɔtrɔ kple woƒe xexlẽdzesiwo wɔwɔ me la akpɔ tagbɔ vevi aɖe le dɔwɔƒewo ƒe AI kpeɖeŋutɔ siwo ateŋu aɖo nyabiase siwo wotu ɖe habɔbɔ ƒe sidzedze dzi si xɔ ƒe bla nanewo dzi la zazã me.

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Nyabiase Siwo Wobiana Enuenu

Ðe mateŋu atrɔ DjVu faɛlwo ɖe nɔnɔme siwo sɔ kple egbegbe AI dɔwɔnuwo mea?

Ẽ. Dɔwɔnu siwo le ʋuʋu ɖi abe DjVuLibre kple asitsalawo ƒe tɔtrɔlawo ateŋu aɖe DjVu faɛlwo gɔme ɖe PDF, TIFF, alo PNG nɔnɔme siwo ŋu nusɔsrɔ̃ deto ƒe ɖoɖo akpa gãtɔ doa alɔe le wo ɖokui si. Le dɔwɔwɔ le agbɔsɔsɔ me gome la, sedede-mɔ̃ ƒe pɔmpiwo ateŋu awɔ tɔtrɔ le wo ɖokui si le nudzraɖoƒe blibowo me, togbɔ be ele be nàda asi ɖe emetsonu ƒe nyonyome dzi le kpɔɖeŋu teƒenɔla dzi hafi awɔ tɔtrɔ gãwo.

Ðe wogale DjVu wɔm vevie loo alo ɖe wònye domenyinu ƒe nɔnɔme?

DjVu nye domenyinu ƒe ɖoɖo koŋ le ɣeyiɣi sia me, eye ŋgɔyiyi si le dɔ wɔm la tɔ te akpa gãtɔ tso ƒe 2000 ƒe domedome. Gake egakpɔtɔ le ezãm geɖe le dijitaal agbalẽdzraɖoƒe ƒe lãwo ƒe agbenɔnɔ ƒe ɖoɖowo me le nyatakaka siwo li xoxo siwo wodzra ɖo ɖe nɔnɔmea me ƒe agbɔsɔsɔme dzro ko ta. Nusɔsrɔ̃ deto le agbe evelia nam DjVu nyuie to ewɔwɔ be wòanye ganyawo ƒe viɖe be woaɖe sidzedze siwo wotu ɖe nudzraɖoƒe siawo me ahazã wo.

Aleke DjVu ƒe nutete sɔ kple PDF na nusɔsrɔ̃ goglo ƒe hehexɔxɔ ŋuti nyatakakawo?

Zi geɖe la, DjVu kpɔa nutete si nyo wu PDF zi gbɔ zi 5–10 na nuŋlɔɖi siwo woskan esime wòléa nukpɔkpɔ ƒe nuteƒewɔwɔ si lolo wu ta le faɛl ƒe lolome siwo sɔ me. Esia na be DjVu-tsoƒe nyatakakadzraɖoƒewo te ŋu dzraa nu ɖo nyuie wu na hehenana pɔmpiwo, togbɔ be ɖoɖoa ƒe kpekpeɖeŋu vevi si le sue wu fia be ehiã be woatsɔ dɔwɔnu bubuwo awɔ dɔe do ŋgɔ ne wotsɔe sɔ kple PDF ƒe lãwo ƒe agbenɔnɔ ƒe ɖoɖo si le afisiafi.

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Dɔwɔnuwo, dɔwɔwɔ ƒe ɖoɖowo, kple sidzedze ƒe ɖoɖo siwo naa ŋusẽ egbegbe AI-ʋuʋu ƒe dɔwɔwɔwo dzi kpɔkpɔ — tso nuŋlɔɖiwo wɔwɔ dzi va ɖo nyatakakawo dzikpɔkpɔ dzi — bia mɔnu si wotu na nusiwo sesẽ le agbɔsɔsɔ me. Mewayz nye asitsadɔwɔɖoɖo si me module 207 le si dzi ezãla siwo wu 138,000 ka ɖe edzi be wòawɔ ɖoɖo ɖe woƒe habɔbɔa ƒe akpa ɖesiaɖe ŋu, eye wòdzea egɔme tso $19/ɣleti ko dzi. Eɖanye be èle nudzraɖoƒewo zãm le dijitaal dzi, èle nuŋlɔɖiwo ƒe dɔwɔwɔ wɔm le wo ɖokui si, alo nèle sidzedze ƒe xɔ siwo ŋu AI yeyetɔ zãna la tum o, Mewayz naa xɔtuɖoɖo wò be nàwɔ wo katã le teƒe ɖeka.

Dze wò Mewayz mɔzɔzɔ gɔme egbea le app.mewayz.com eye nàke ɖe alesi asitsatsa ƒe OS si wɔ ɖeka trɔa alesi wò ƒuƒoƒoa wɔa dɔe, dzidzea nu, kple nu yeyewo dodo ɖe ŋgɔe ŋu.