{"id":69,"date":"2026-05-18T12:37:30","date_gmt":"2026-05-18T12:37:30","guid":{"rendered":"https:\/\/convly.ai\/best-ocr-tools-2026\/"},"modified":"2026-06-10T05:06:02","modified_gmt":"2026-06-10T05:06:02","slug":"best-ocr-tools-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/","title":{"rendered":"The Best OCR Tools in 2026: 10 Picks for Document Processing"},"content":{"rendered":"<p>OCR \u2014 optical character recognition \u2014 used to mean one thing: convert a scan into text. In 2026 it means something bigger. AI vision models don&#8217;t just <em>read<\/em> a document, they <em>understand<\/em> it: they extract the line items from an invoice, the fields from a form, the structure of a table, and they do it on messy, handwritten, multi-language pages that broke traditional OCR for decades.<\/p>\n<p>That shift split the market into two camps: classic OCR engines and AI document models. We tested both and ranked the 10 best tools for turning documents into usable data.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principais conclus\u00f5es<\/h3>\n<ul>\n<li><strong>Best overall accuracy:<\/strong> AI vision models \u2014 Gemini, GPT-4o, and dedicated OCR APIs like Mistral OCR \u2014 now beat classic engines on hard documents.<\/li>\n<li><strong>Best dedicated OCR API:<\/strong> Mistral OCR \u2014 fast, cheap, and built specifically for the job.<\/li>\n<li><strong>Best for enterprise pipelines:<\/strong> Google Document AI, Azure AI Document Intelligence, Amazon Textract.<\/li>\n<li><strong>Best free \/ open-source:<\/strong> Tesseract for simple text, Surya and PaddleOCR for modern layouts.<\/li>\n<li><strong>Best for handwriting &amp; messy scans:<\/strong> any AI vision model \u2014 this is where they crush old OCR.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a38b81a045c6\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Alternar<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a38b81a045c6\"  aria-label=\"Alternar\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#What_changed_AI_ate_OCR\" >What changed: AI ate OCR<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#What_to_judge_an_OCR_tool_on\" >What to judge an OCR tool on<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#The_10_best_OCR_tools\" >The 10 best OCR tools<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#Side-by-side_comparison\" >Side-by-side comparison<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#How_to_choose\" >Como escolher<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#A_note_on_accuracy_and_validation\" >A note on accuracy and validation<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#What_OCR_actually_costs_the_three_pricing_models\" >What OCR actually costs: the three pricing models<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#FAQ\" >Perguntas frequentes<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#Bottom_line\" >Conclus\u00e3o<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/pt\/best-ocr-tools-2026\/#Related_articles\" >Artigos relacionados<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_changed_AI_ate_OCR\"><\/span>What changed: AI ate OCR<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Traditional OCR engines pattern-match shapes to characters. They&#8217;re fast and reliable on clean, printed, single-column text \u2014 and they fall apart on handwriting, complex tables, poor scans, unusual layouts, and mixed languages.<\/p>\n<p>AI vision models read a document the way a person does: in context. They infer a smudged digit from the surrounding numbers, understand that a block of text is a table and preserve its structure, and handle handwriting that classic OCR can&#8217;t touch. The cost is that they can occasionally &#8220;hallucinate&#8221; a plausible-but-wrong value, so high-stakes pipelines still need validation. But for accuracy on real-world documents, AI OCR is now ahead.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_to_judge_an_OCR_tool_on\"><\/span>What to judge an OCR tool on<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ol>\n<li><strong>Accuracy<\/strong> \u2014 on clean text, handwriting, tables, and poor scans.<\/li>\n<li><strong>Layout understanding<\/strong> \u2014 does it preserve structure, or return a wall of text?<\/li>\n<li><strong>Structured extraction<\/strong> \u2014 can it pull specific fields (totals, dates, IDs) directly?<\/li>\n<li><strong>Languages<\/strong> \u2014 coverage beyond English, including non-Latin scripts.<\/li>\n<li><strong>Integration<\/strong> \u2014 API, batch processing, output formats.<\/li>\n<li><strong>Cost and privacy<\/strong> \u2014 per-page pricing, and whether documents leave your infrastructure.<\/li>\n<\/ol>\n<h2><span class=\"ez-toc-section\" id=\"The_10_best_OCR_tools\"><\/span>The 10 best OCR tools<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>1. Mistral OCR \u2014 best dedicated OCR API<\/h3>\n<p>A purpose-built OCR API that&#8217;s fast, inexpensive, and accurate. It handles complex layouts, tables, and equations, and returns clean structured output. For developers who want OCR as a focused service \u2014 not a general chatbot \u2014 this is the standout pick.<\/p>\n<h3>2. Google Gemini \/ Document AI \u2014 best for understanding<\/h3>\n<p>Gemini&#8217;s vision capabilities make it superb at <em>understanding<\/em> documents, not just transcribing them. For production pipelines, Google&#8217;s Document AI platform adds pre-built parsers for invoices, receipts, and forms. The combination covers everything from one-off extraction to enterprise-scale processing.<\/p>\n<h3>3. GPT-4o \u2014 best general-purpose AI OCR<\/h3>\n<p>GPT-4o&#8217;s vision reads documents with excellent accuracy and, crucially, lets you <em>ask<\/em> for exactly what you need: &#8220;extract every line item as JSON.&#8221; It&#8217;s the most flexible tool when your extraction needs vary from document to document.<\/p>\n<h3>4. Claude \u2014 best for complex, reasoning-heavy documents<\/h3>\n<p>Claude&#8217;s vision is excellent on dense, structured, or reasoning-heavy documents \u2014 long contracts, technical reports, multi-table pages. When you need the tool to interpret as well as transcribe, it&#8217;s a top choice.<\/p>\n<h3>5. Azure AI Document Intelligence \u2014 best Microsoft-stack option<\/h3>\n<p>Microsoft&#8217;s document service offers strong prebuilt models (invoices, receipts, IDs), custom model training, and tight integration with the Azure ecosystem. The default for organizations already on Microsoft cloud.<\/p>\n<h3>6. Amazon Textract \u2014 best for AWS pipelines<\/h3>\n<p>Textract extracts text, forms, and tables at scale with reliable structured output. If your data pipeline lives in AWS, it integrates cleanly and handles high volumes well.<\/p>\n<h3>7. ABBYY FineReader \u2014 best traditional enterprise OCR<\/h3>\n<p>The long-standing enterprise OCR leader. FineReader is highly accurate on printed documents, supports a vast range of languages, and offers desktop and server products with mature document-conversion workflows. Strong where on-premise processing is required.<\/p>\n<h3>8. Adobe Acrobat \u2014 best for everyday PDF OCR<\/h3>\n<p>For individuals and offices, Acrobat&#8217;s built-in OCR turns scanned PDFs into searchable, editable documents with no setup. Not an extraction platform, but the most convenient tool for routine PDF work.<\/p>\n<h3>9. Tesseract \u2014 best free open-source engine<\/h3>\n<p>The most established open-source OCR engine. Free, self-hostable, supports 100+ languages, and completely private. It&#8217;s weaker on complex layouts and handwriting, but for clean printed text at zero cost, it&#8217;s still a workhorse.<\/p>\n<h3>10. Surya &amp; PaddleOCR \u2014 best modern open-source<\/h3>\n<p>Two newer open-source projects that handle modern layouts, tables, and many languages far better than Tesseract. The best free option when you need structure-aware OCR you can run yourself. (For math and scientific notation specifically, <strong>Mathpix<\/strong> is the specialist worth knowing.)<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Side-by-side_comparison\"><\/span>Side-by-side comparison<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Ferramenta<\/th>\n<th>Tipo<\/th>\n<th>Handwriting<\/th>\n<th>Structured extraction<\/th>\n<th>Melhor para<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Mistral OCR<\/td>\n<td>AI OCR API<\/td>\n<td>Fortes<\/td>\n<td>Sim<\/td>\n<td>Developers<\/td>\n<\/tr>\n<tr>\n<td>Gemini \/ Document AI<\/td>\n<td>AI + platform<\/td>\n<td>Fortes<\/td>\n<td>Sim<\/td>\n<td>Enterprise pipelines<\/td>\n<\/tr>\n<tr>\n<td>GPT-4o<\/td>\n<td>AI vision<\/td>\n<td>Fortes<\/td>\n<td>Yes (flexible)<\/td>\n<td>General-purpose<\/td>\n<\/tr>\n<tr>\n<td>Azure \/ Textract<\/td>\n<td>API em nuvem<\/td>\n<td>Bom<\/td>\n<td>Sim<\/td>\n<td>Cloud-stack teams<\/td>\n<\/tr>\n<tr>\n<td>ABBYY FineReader<\/td>\n<td>Classic OCR<\/td>\n<td>Limitado<\/td>\n<td>Forms<\/td>\n<td>On-premise enterprise<\/td>\n<\/tr>\n<tr>\n<td>Tesseract<\/td>\n<td>Open-source<\/td>\n<td>Fraco<\/td>\n<td>N\u00e3o<\/td>\n<td>Free printed-text OCR<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"How_to_choose\"><\/span>Como escolher<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>You&#8217;re a developer who wants OCR as a service:<\/strong> Mistral OCR, or GPT-4o for flexible extraction.<\/li>\n<li><strong>You&#8217;re building an enterprise document pipeline:<\/strong> Google Document AI, Azure AI Document Intelligence, or Amazon Textract \u2014 match your cloud.<\/li>\n<li><strong>You process printed documents on-premise:<\/strong> ABBYY FineReader.<\/li>\n<li><strong>You just need searchable PDFs:<\/strong> Adobe Acrobat.<\/li>\n<li><strong>You want free and private:<\/strong> Tesseract for simple text, Surya or PaddleOCR for modern layouts.<\/li>\n<li><strong>Your documents have handwriting or messy scans:<\/strong> any AI vision model \u2014 that&#8217;s their advantage.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"A_note_on_accuracy_and_validation\"><\/span>A note on accuracy and validation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>AI OCR is more accurate than classic OCR on hard documents, but it has a different failure mode: instead of returning a garbled character, it may confidently return a wrong-but-plausible value. For low-stakes work this is fine. For invoices, financial data, medical records, or legal documents, build a validation step: confidence checks, business rules (totals must add up), or human review of flagged extractions. Treat AI OCR as a fast first pass, not an unchecked source of truth.<\/p>\n<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_OCR_actually_costs_the_three_pricing_models\"><\/span>What OCR actually costs: the three pricing models<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The true cost of OCR is rarely the headline price, and the cheapest option per page is almost never the cheapest in practice. Several different billing models compete in 2026, and the right one depends entirely on your volume and document type.<\/p>\n<p><strong>Dedicated OCR APIs<\/strong> charge per page, and the field has converged tightly. Mistral&#8217;s OCR runs about $2 per 1,000 pages (roughly half that on its batch tier), while Amazon Textract, Azure AI Document Intelligence and Google Document AI all sit near $1.50 per 1,000 pages for plain-text extraction, dropping toward $0.60 at multi-million-page volumes. Structured extraction (invoices, forms, tables) costs many times more on most platforms &#8211; often 20x to 30x the plain-text rate &#8211; so the feature you enable can matter more than the provider you pick.<\/p>\n<p><strong>General-purpose LLMs<\/strong> like GPT-4o, Claude and Gemini bill per token, not per page, which changes the math entirely. A dense page can consume thousands of input tokens plus the output, and high-resolution images are tiled into many more tokens still. For a handful of complex documents the convenience is worth it, but at scale a per-token model can cost several times more than a dedicated OCR API for the same pages. Reserve the frontier models for documents that genuinely need reasoning, and route bulk text through a per-page engine.<\/p>\n<p><strong>Open-source engines<\/strong> (Tesseract, Surya, PaddleOCR) carry no licence fee, but free is not zero. Your cost is the GPU or CPU time to run them, the engineering hours to build and maintain the pipeline, and the accuracy gap you may need to close with manual review. Below a few thousand pages a month, a hosted API is almost always cheaper once you price in your own time. Above that, self-hosting starts to pay off, especially for sensitive data that cannot leave your servers.<\/p>\n<p><strong>Desktop tools<\/strong> like ABBYY FineReader and Adobe Acrobat use a third model: a per-seat licence &#8211; billed as an annual subscription, or a one-off perpetual purchase where offered &#8211; with unlimited local processing. For a single user digitising documents at a desk, that flat fee beats any per-page API. The break-even logic is simple: low volume favours a desktop licence, steady mid-volume favours a per-page API, and very high or privacy-bound volume favours self-hosting.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>Perguntas frequentes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What is the most accurate OCR tool in 2026?<\/h3>\n<p>For real-world documents \u2014 handwriting, tables, poor scans, mixed languages \u2014 AI vision models like Gemini, GPT-4o, and dedicated APIs such as Mistral OCR are now the most accurate. For clean printed text, classic engines like ABBYY FineReader remain excellent and fast.<\/p>\n<h3>Is there a good free OCR tool?<\/h3>\n<p>Yes. Tesseract is the established free, open-source engine for printed text in 100+ languages. Surya and PaddleOCR are newer open-source projects that handle modern layouts and tables much better. All three run on your own hardware, so they&#8217;re free and private.<\/p>\n<h3>Can AI OCR read handwriting?<\/h3>\n<p>Yes \u2014 this is where AI vision models clearly beat traditional OCR. Models like GPT-4o, Gemini, and Claude can read handwritten notes, forms, and messy scans with good accuracy, because they infer characters from context rather than matching shapes in isolation.<\/p>\n<h3>What is the difference between OCR and AI document processing?<\/h3>\n<p>OCR converts an image of text into machine-readable text. AI document processing goes further: it understands the document&#8217;s structure and meaning \u2014 identifying tables, extracting specific fields, and returning organized data. In 2026 the best tools do both in one step.<\/p>\n<h3>Is it safe to send documents to cloud OCR services?<\/h3>\n<p>For non-sensitive documents, the major providers are generally safe and offer business agreements covering data handling. For confidential material \u2014 medical, legal, financial \u2014 review the provider&#8217;s data terms, use an enterprise tier, or run an open-source tool like Tesseract or PaddleOCR locally so documents never leave your infrastructure.<\/p>\n<h3>Is it cheaper to use a dedicated OCR API or an LLM like GPT-4o?<\/h3>\n<p>For volume work, a dedicated OCR API is far cheaper. Engines like Mistral&#8217;s OCR or Amazon Textract bill per page (roughly $1.50 to $2 per 1,000 pages for plain text), while GPT-4o, Claude and Gemini bill per token. Because a single dense page can burn thousands of tokens, an LLM often costs several times more per page at scale. Use frontier models only when a document needs genuine reasoning or layout understanding the dedicated engines cannot provide; route everything else through a per-page OCR API.<\/p>\n<h3>What is the cheapest way to OCR thousands of documents?<\/h3>\n<p>Batch processing is the lever. Most cloud OCR APIs offer asynchronous or batch endpoints that cut the per-page rate substantially (Mistral, for example, roughly halves its price for batch jobs), and per-page rates fall further at high volume. For very large, recurring, or privacy-sensitive workloads, self-hosting an open-source engine like PaddleOCR or Surya on your own GPU can be cheaper still, provided you have the engineering capacity to run and maintain it.<\/p>\n<h3>Can OCR tools read non-English and non-Latin scripts?<\/h3>\n<p>Yes, though coverage varies. The leading cloud engines and AI models handle dozens to hundreds of languages, including non-Latin scripts such as Arabic, Chinese, Japanese, Korean and Cyrillic, and the strongest AI OCR models read mixed-language documents well. Tesseract supports 100-plus languages but needs the correct language pack installed, and accuracy on complex or right-to-left scripts still trails the best AI systems. If your documents are multilingual, test on real samples before committing.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Conclus\u00e3o<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>OCR in 2026 is really two markets. For <strong>understanding messy, real-world documents<\/strong> \u2014 handwriting, tables, bad scans \u2014 AI vision models win: use Mistral OCR or GPT-4o as a developer, or Google Document AI, Azure, or Textract for enterprise pipelines. For <strong>clean printed text and on-premise needs<\/strong>, classic tools like ABBYY FineReader still deliver. And for <strong>free, private processing<\/strong>, Tesseract, Surya, and PaddleOCR cover most needs at zero cost.<\/p>\n<p>Pick by document type and where your data is allowed to go \u2014 and for anything high-stakes, add a validation step. The reading is solved; the checking is still on you.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Artigos relacionados<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/pt\/veo-3-vs-kling-3-for-ai-video-2026\/\">Veo 3.1 vs Kling 3.0 para v\u00eddeos com IA em 2026: qual deles vence em realismo?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/best-cloud-gpu-providers-for-ai-2026\/\">Melhores provedores de GPU em nuvem para IA em 2026: RunPod, Lambda, Vast, Together e Replicate<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/ai-translation-tools-compared\/\">The Best AI Translation Tools in 2026: DeepL vs Google vs ChatGPT<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/ai-music-generators-suno-vs-udio\/\">AI Music Generators in 2026: Suno vs Udio (Hands-On Review)<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>OCR was quietly transformed by AI vision models in 2026. We rank the 10 best tools for turning documents into data \u2014 from LLM-based OCR to cloud APIs and free open-source options.<\/p>","protected":false},"author":0,"featured_media":70,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[5],"tags":[390,387,388,389,386],"class_list":["post-69","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-tools","tag-ai-document-processing","tag-best-ocr-tools","tag-document-ai","tag-ocr-api","tag-ocr-software"],"_links":{"self":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/69","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/comments?post=69"}],"version-history":[{"count":3,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/69\/revisions"}],"predecessor-version":[{"id":1037,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/69\/revisions\/1037"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media\/70"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media?parent=69"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/categories?post=69"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/tags?post=69"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}