{"id":1879,"date":"2026-02-17T16:11:57","date_gmt":"2026-02-17T14:11:57","guid":{"rendered":"https:\/\/parserdata.com\/blog\/?p=1879"},"modified":"2026-03-10T21:37:17","modified_gmt":"2026-03-10T19:37:17","slug":"how-to-extract-data-from-documents","status":"publish","type":"post","link":"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/","title":{"rendered":"How to Extract Data from Documents: The Ultimate 2026 Guide"},"content":{"rendered":"\n<p>Every day, businesses generate 2.5 quintillion bytes of data. Yet, a massive portion of this value is trapped in unstructured formats: invoices, contracts, resumes, and forms. For modern organizations, the ability to unlock this information is not just a technical skill it is a competitive necessity. This brings us to the critical question: <strong>how to extract data from documents<\/strong> accurately, efficiently, and at scale?<\/p>\n\n\n\n<p>According to <a href=\"https:\/\/www.idc.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">IDC<\/a>, organizations that analyze all relevant data and deliver actionable information achieve an extra $430 billion in productivity gains over their less analytical peers. But you cannot analyze what you cannot read.<\/p>\n\n\n\n<p>In this master guide, we will move beyond the basics. We will explore <strong>how to extract data from documents<\/strong> using the latest AI technologies, comparing manual entry, template-based OCR, and next-generation Intelligent Document Processing (IDP). Whether you are a developer looking for an API or a CFO seeking ROI, this guide is your roadmap.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Table of Contents<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"#The-Problem-Why\">1. The Problem: Why &#8220;Ctrl+C&#8221; Isn&#8217;t Enough<\/a><\/li>\n\n\n\n<li><a href=\"#The-3-Evolution-Stages-of-Extraction\">2. The 3 Evolution Stages of Extraction<\/a><\/li>\n\n\n\n<li><a href=\"#Audit-Your-Document-Ecosystem\">3. Step 1: Audit Your Document Ecosystem<\/a><\/li>\n\n\n\n<li><a href=\"#Select-the-Right-Technology\" target=\"_blank\" rel=\"noreferrer noopener\">4. Step 2: Select the Right Technology<\/a><\/li>\n\n\n\n<li><a href=\"#Configure-Your-Extraction-Schema\">5. Step 3: Configure Your Extraction Schema<\/a><\/li>\n\n\n\n<li><a href=\"#Validation-Quality-Control\" target=\"_blank\" rel=\"noreferrer noopener\">6. Step 4: Validation &amp; Quality Control<\/a><\/li>\n\n\n\n<li><a href=\"#Integration-Automation\">7. Step 5: Integration &amp; Automation<\/a><\/li>\n\n\n\n<li><a href=\"#Technical-Deep-Dive\">8. Technical Deep Dive: Handling Tables &amp; Handwriting<\/a><\/li>\n\n\n\n<li><a href=\"#Comparison-Python-VS-No-Code-Tools\">9. Comparison: Python vs. No-Code Tools<\/a><\/li>\n\n\n\n<li><a href=\"#Future-Trends-Conclusion\">10. Future Trends &amp; Conclusion<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"The-Problem-Why\">1. The Problem: Why &#8220;Ctrl+C&#8221; Isn&#8217;t Enough<\/h2>\n\n\n\n<p>When you ask <strong>how to extract data from documents<\/strong>, you are essentially asking how to turn &#8220;unstructured&#8221; content into &#8220;structured&#8221; data.<\/p>\n\n\n\n<p>A PDF invoice looks organized to a human eye. We see a table, a total, and a date. To a computer, however, a standard PDF is just a map of coordinates: <em>&#8220;Place letter &#8216;T&#8217; at X:100, Y:200&#8221;<\/em>. It doesn&#8217;t know that &#8216;T&#8217; is part of the word &#8220;Total&#8221;.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The &#8220;Dark Data&#8221; Challenge<\/h3>\n\n\n\n<p>This trapped information is called &#8220;Dark Data.&#8221; If you rely on manual copy-pasting, you face three risks:<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Scalability:<\/strong> You cannot hire enough humans to copy-paste 10,000 invoices a month.<\/li>\n\n\n\n<li><strong>Accuracy:<\/strong> Humans have a 4% error rate. In finance, a typo in an invoice number creates reconciliation hell.<\/li>\n\n\n\n<li><strong>Speed:<\/strong> Manual entry takes days. Automated extraction takes seconds.<\/li>\n<\/ol>\n\n\n\n<p><em>Stop typing manually! Here is a real-world example of extracting document data in seconds \ud83d\udc47<\/em><\/p>\n\n\n<style>.glightbox-kadence-dark.kadence-popup-1879_022ebe-a0 .goverlay{background:#000000;opacity:0.8;}.glightbox-container.kadence-popup-1879_022ebe-a0 .gclose path, .glightbox-container.kadence-popup-1879_022ebe-a0 .gnext path, .glightbox-container.kadence-popup-1879_022ebe-a0 .gprev path{fill:#ffffff;}.glightbox-container.kadence-popup-1879_022ebe-a0 .gslide-video, .glightbox-container.kadence-popup-1879_022ebe-a0 .gvideo-local{max-width:900px !important;}<\/style>\n<div class=\"wp-block-kadence-videopopup kadence-video-popup1879_022ebe-a0\"><div class=\"kadence-video-popup-wrap kadence-video-noshadow\"><div class=\"kadence-video-intrinsic \"><img decoding=\"async\" data-src=\"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2025\/09\/PDF-to-Excel-AI.png\" alt=\"Convert PDF to Excel with AI in Seconds\" width=\"1024\" height=\"576\" class=\"kadence-video-poster wp-image-2165 lazyload\" data-srcset=\"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2025\/09\/PDF-to-Excel-AI.png 1024w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2025\/09\/PDF-to-Excel-AI-300x169.png 300w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2025\/09\/PDF-to-Excel-AI-768x432.png 768w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/576;\" \/><div class=\"kadence-video-overlay\"><\/div><a class=\"kadence-video-popup-link kadence-video-type-external\" aria-label=\"Tutorial: Converting PDF invoices to Excel automatically using ParserData AI\" href=\"https:\/\/youtu.be\/bhLdwYGMg2o?si=caGQgQjTjhT4lZsC\" role=\"button\" data-popup-class=\"kadence-popup-1879_022ebe-a0\" data-effect=\"none\" data-popup-id=\"kadence-local-video-1879_022ebe-a0\" data-popup-auto=\"false\" data-youtube-cookies=\"true\"><span class=\"kb-svg-icon-wrap kb-svg-icon-fas_play kt-video-svg-icon kt-video-svg-icon-style-default kt-video-svg-icon-fas play kt-video-play-animation-none kt-video-svg-icon-size-auto\"><svg viewBox=\"0 0 448 512\"  fill=\"currentColor\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"  role=\"img\"><title>Play<\/title><path d=\"M424.4 214.7L72.4 6.6C43.8-10.3 0 6.1 0 47.9V464c0 37.5 40.7 60.1 72.4 41.3l352-208c31.4-18.5 31.5-64.1 0-82.6z\"\/><\/svg><\/span><\/a><\/div><\/div><\/div>\n\n\n\n<p>Learning <strong>how to extract data from documents<\/strong> using automation solves these problems, converting static files into a live stream of <a href=\"https:\/\/parserdata.com\/blog\/why-integrate-data-analytics\/\" target=\"_blank\" rel=\"noreferrer noopener\">integrated data analytics<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"The-3-Evolution-Stages-of-Extraction\">2. The 3 Evolution Stages of Extraction<\/h2>\n\n\n\n<p>To understand <strong>how to extract data from documents<\/strong> effectively in 2026, you must know the tools available. We have evolved through three distinct eras.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Era 1: Manual Entry<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Method:<\/strong> Humans typing into Excel.<\/li>\n\n\n\n<li><strong>Pros:<\/strong> High cognitive understanding (humans understand context).<\/li>\n\n\n\n<li><strong>Cons:<\/strong> Slow, expensive, error-prone.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Era 2: Zonal OCR (Templates)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Method:<\/strong> You draw a box on the screen and tell the software: <em>&#8220;Read the text in this box.&#8221;<\/em><\/li>\n\n\n\n<li><strong>Pros:<\/strong> Fast for fixed forms (like tax forms).<\/li>\n\n\n\n<li><strong>Cons:<\/strong> Brittle. If the vendor moves their logo, the box reads empty space. This is a common pitfall when learning <strong>how to extract data from documents<\/strong>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Era 3: Intelligent Document Processing (IDP)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Method:<\/strong> AI and Machine Learning. The software reads the whole page and looks for the <em>meaning<\/em>. It finds &#8220;Total Amount&#8221; whether it&#8217;s at the top, bottom, or middle.<\/li>\n\n\n\n<li><strong>Pros:<\/strong> Flexible, scalable, handles complex tables.<\/li>\n\n\n\n<li><strong>Tools:<\/strong> Platforms like <strong>ParserData<\/strong>.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1024\" height=\"1024\" data-src=\"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-the-evolution-of-methods-for-how-to-extract-data-from-documents.jpg\" alt=\"Diagram showing the evolution of methods for how to extract data from documents\" class=\"wp-image-1887 lazyload\" data-srcset=\"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-the-evolution-of-methods-for-how-to-extract-data-from-documents.jpg 1024w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-the-evolution-of-methods-for-how-to-extract-data-from-documents-300x300.jpg 300w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-the-evolution-of-methods-for-how-to-extract-data-from-documents-150x150.jpg 150w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-the-evolution-of-methods-for-how-to-extract-data-from-documents-768x768.jpg 768w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/1024;\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"Audit-Your-Document-Ecosystem\">3. Step 1: Audit Your Document Ecosystem<\/h2>\n\n\n\n<p>The first practical step in learning <strong>how to extract data from documents<\/strong> is not buying software\u2014it&#8217;s auditing your files. You cannot automate what you don&#8217;t understand.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Categorize by Variability<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Structured Documents:<\/strong> Fixed forms (W-2, Surveys). The layout never changes. <em>Difficulty: Low.<\/em><\/li>\n\n\n\n<li><strong>Semi-Structured Documents:<\/strong> Invoices, Purchase Orders, Receipts. The data is the same (Date, Total), but the layout varies by vendor. <em>Difficulty: Medium.<\/em><\/li>\n\n\n\n<li><strong>Unstructured Documents:<\/strong> Contracts, Emails, Letters. Dense text with no clear layout. <em>Difficulty: High.<\/em><\/li>\n<\/ul>\n\n\n\n<p><strong>Pro Tip:<\/strong> Start by learning <strong>how to extract data from documents<\/strong> in the &#8220;Semi-Structured&#8221; category (like Invoices). This offers the highest ROI. (See our list of <a href=\"https:\/\/parserdata.com\/blog\/types-of-business-documents-to-automate\/\" target=\"_blank\" rel=\"noreferrer noopener\">25 types of business documents to automate<\/a>).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"Select-the-Right-Technology\">4. Step 2: Select the Right Technology<\/h2>\n\n\n\n<p>Once you know <em>what<\/em> you are processing, you must choose <em>how<\/em> to extract data from documents.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>For Low Volume (&lt;50\/month):<\/strong> Manual entry or free online tools might suffice.<\/li>\n\n\n\n<li><strong>For High Volume (&gt;1000\/month):<\/strong> You need an API-based IDP solution.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Why API First?<\/h3>\n\n\n\n<p>If you are building a scalable workflow, do not use desktop software. Use a cloud API. This allows your ERP or CRM to automatically send files for extraction and receive JSON back. Read more about the <a href=\"https:\/\/parserdata.com\/blog\/role-of-api-in-automation\/\" target=\"_blank\" rel=\"noreferrer noopener\">role of API in automation<\/a>.<\/p>\n\n\n<style>.wp-block-kadence-advancedbtn.kb-btns1879_5c9195-3e{gap:var(--global-kb-gap-xs, 0.5rem );justify-content:center;align-items:center;}.kt-btns1879_5c9195-3e .kt-button{font-weight:normal;font-style:normal;}.kt-btns1879_5c9195-3e .kt-btn-wrap-0{margin-right:5px;}.wp-block-kadence-advancedbtn.kt-btns1879_5c9195-3e .kt-btn-wrap-0 .kt-button{color:#555555;border-color:#555555;}.wp-block-kadence-advancedbtn.kt-btns1879_5c9195-3e .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns1879_5c9195-3e .kt-btn-wrap-0 .kt-button:focus{color:#ffffff;border-color:#444444;}.wp-block-kadence-advancedbtn.kt-btns1879_5c9195-3e .kt-btn-wrap-0 .kt-button::before{display:none;}.wp-block-kadence-advancedbtn.kt-btns1879_5c9195-3e .kt-btn-wrap-0 .kt-button:hover, .wp-block-kadence-advancedbtn.kt-btns1879_5c9195-3e .kt-btn-wrap-0 .kt-button:focus{background:#444444;}<\/style>\n<div class=\"wp-block-kadence-advancedbtn kb-buttons-wrap kb-btns1879_5c9195-3e\"><style>ul.menu .wp-block-kadence-advancedbtn .kb-btn1879_badd88-72.kb-button{width:initial;}<\/style><a class=\"kb-button kt-button button kb-btn1879_badd88-72 kt-btn-size-standard kt-btn-width-type-auto kb-btn-global-fill  kt-btn-has-text-true kt-btn-has-svg-false  wp-block-kadence-singlebtn\" href=\"https:\/\/parserdata.com\/pricing\"><span class=\"kt-btn-inner-text\">Try for FREE<\/span><\/a><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"Configure-Your-Extraction-Schema\">5. Step 3: Configure Your Extraction Schema<\/h2>\n\n\n\n<p>This is the most critical technical step. When defining <strong>how to extract data from documents<\/strong>, you must tell the AI exactly what you want. This is called a &#8220;Schema&#8221; or &#8220;Model.&#8221;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Defining Key-Value Pairs<\/h3>\n\n\n\n<p>You don&#8217;t want &#8220;all the text.&#8221; You want specific fields.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Target:<\/strong> <code>Invoice Number<\/code> | <strong>Type:<\/strong> String<\/li>\n\n\n\n<li><strong>Target:<\/strong> <code>Total Amount<\/code> | <strong>Type:<\/strong> Number (Currency)<\/li>\n\n\n\n<li><strong>Target:<\/strong> <code>Issue Date<\/code> | <strong>Type:<\/strong> Date (Normalized to YYYY-MM-DD)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">The Taxonomy of Data<\/h3>\n\n\n\n<p>Be consistent. If one vendor calls it &#8220;Due Date&#8221; and another calls it &#8220;Payment Date,&#8221; your schema should map both to a single database field: <code>payment_due_date<\/code>. This standardization is the secret sauce of <strong>how to extract data from documents<\/strong> successfully across multiple vendors.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"Validation-Quality-Control\">6. Step 4: Validation &amp; Quality Control<\/h2>\n\n\n\n<p>Extracting data is easy; trusting it is hard. If you don&#8217;t know <strong>how to extract data from documents<\/strong> with validation gates, you risk polluting your database with bad data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Confidence Score<\/h3>\n\n\n\n<p>Modern AI tools like <strong>ParserData<\/strong> provide a &#8220;Confidence Score&#8221; (0-100%) for every field.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Rule:<\/strong> If Confidence &lt; 80%, route the document to a human for manual review (&#8220;Human-in-the-Loop&#8221;).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Logical Validation Rules<\/h3>\n\n\n\n<p>Don&#8217;t just rely on the AI. Use math to catch errors.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Math Check:<\/strong> Does <code>Subtotal + Tax<\/code> actually equal <code>Total<\/code>? If not, flag the document.<\/li>\n\n\n\n<li><strong>Format Check:<\/strong> Is the &#8220;Invoice Date&#8221; in the future? Is the &#8220;Total&#8221; negative?<\/li>\n\n\n\n<li><strong>Database Match:<\/strong> Does the extracted &#8220;Vendor Name&#8221; exist in your approved vendor list?<\/li>\n<\/ul>\n\n\n\n<p>Implementing these rules is the difference between a toy project and an enterprise-grade solution for <strong>how to extract data from documents<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"Integration-Automation\">7. Step 5: Integration &amp; Automation<\/h2>\n\n\n\n<p>The final step in understanding <strong>how to extract data from documents<\/strong> is moving the data from the &#8220;Extraction Layer&#8221; to the &#8220;Business Layer.&#8221;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Webhooks &amp; APIs<\/h3>\n\n\n\n<p>You shouldn&#8217;t be downloading CSVs manually. Set up Webhooks.<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Trigger:<\/strong> An email arrives with a PDF.<\/li>\n\n\n\n<li><strong>Action:<\/strong> The PDF is sent to the ParserData API.<\/li>\n\n\n\n<li><strong>Callback:<\/strong> When extraction is done (seconds later), a Webhook pushes the JSON payload directly to your ERP (SAP, Oracle, NetSuite) or integration platform (Zapier, Make).<\/li>\n<\/ol>\n\n\n\n<p>This creates a &#8220;Touchless Workflow&#8221; where humans only intervene when exceptions occur.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Quick Comparison: Which Method Fits Your Needs?<\/h2>\n\n\n\n<p>Deciding <strong>how to extract data from documents<\/strong> depends on your volume and complexity. Use this comparison to choose the right stack.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Feature<\/th><th>Manual Entry<\/th><th>Zonal OCR (Templates)<\/th><th>Cognitive AI (IDP)<\/th><\/tr><\/thead><tbody><tr><td><strong>Setup Time<\/strong><\/td><td>None (Start immediately)<\/td><td>High (Draw boxes for each vendor)<\/td><td>Low (Pre-trained models)<\/td><\/tr><tr><td><strong>Accuracy<\/strong><\/td><td>96% (Human error)<\/td><td>98% (If layout is fixed)<\/td><td>99%+ (With validation)<\/td><\/tr><tr><td><strong>Scalability<\/strong><\/td><td>Very Low<\/td><td>Medium<\/td><td>Unlimited (Cloud)<\/td><\/tr><tr><td><strong>Handling Variation<\/strong><\/td><td>Excellent<\/td><td>Fails completely<\/td><td>Excellent (Context aware)<\/td><\/tr><tr><td><strong>Best For<\/strong><\/td><td>&lt; 50 docs\/month<\/td><td>Fixed Government Forms<\/td><td>Invoices, Receipts, Contracts<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Table: Comparing the three main approaches to data extraction.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Pro Tips for Extraction Success<\/h2>\n\n\n\n<p>Mastering <strong>how to extract data from documents<\/strong> requires more than just software. Follow these three golden rules used by enterprise data teams.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<h3 class=\"wp-block-heading\">\ud83d\udca1 Tip 1: Pre-process Your Images<\/h3>\n\n\n\n<p>Garbage in, garbage out. Before sending a scanned PDF to an OCR engine, apply &#8220;Binarization&#8221; (convert to black and white) and &#8220;Deskewing&#8221; (straighten the image). This simple step can boost accuracy by 20%.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\ud83d\udca1 Tip 2: The &#8220;Confidence Threshold&#8221; Strategy<\/h3>\n\n\n\n<p>Don&#8217;t aim for 100% automation immediately. Configure your system to auto-approve any document with a confidence score &gt; 95%. Route anything between 70-95% to a human reviewer. This balances speed with data integrity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\ud83d\udca1 Tip 3: Don&#8217;t hard-code logic for every vendor<\/h3>\n\n\n\n<p>Beginners often write code like <code>if vendor == \"Amazon\": look_at_row_5<\/code>. This is a trap. Instead, use semantic models that look for the <em>label<\/em> &#8220;Total&#8221; near a currency symbol, regardless of the vendor. This makes your system resilient to new layouts.<\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"Technical-Deep-Dive\">8. Technical Deep Dive: Handling Tables &amp; Handwriting<\/h2>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1024\" height=\"939\" data-src=\"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-how-to-extract-data-from-documents-with-multi-page-tables-using-stitching-technology-1.jpg\" alt=\"Diagram showing how to extract data from documents with multi-page tables using stitching technology\" class=\"wp-image-1885 lazyload\" data-srcset=\"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-how-to-extract-data-from-documents-with-multi-page-tables-using-stitching-technology-1.jpg 1024w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-how-to-extract-data-from-documents-with-multi-page-tables-using-stitching-technology-1-300x275.jpg 300w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Diagram-showing-how-to-extract-data-from-documents-with-multi-page-tables-using-stitching-technology-1-768x704.jpg 768w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/939;\" \/><\/figure>\n\n\n\n<p>Most tutorials on <strong>how to extract data from documents<\/strong> skip the hard parts. Let&#8217;s cover them.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Parsing Multi-Page Tables<\/h3>\n\n\n\n<p>Tables often break across pages. The header is on Page 1, but the total is on Page 2.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>The Solution:<\/strong> Use extraction tools with &#8220;Table Stitching&#8221; capabilities. They identify the table structure (grid lines or whitespace) and merge rows from multiple pages into a single dataset.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Handwriting Recognition (ICR)<\/h3>\n\n\n\n<p>Standard OCR reads machine fonts. To read a handwritten signature or a waiter&#8217;s tip on a receipt, you need ICR (Intelligent Character Recognition).<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>The Tech:<\/strong> ICR uses neural networks trained on millions of handwriting samples to decipher cursive script. This is essential for <a href=\"https:\/\/parserdata.com\/blog\/types-of-financial-data-extraction\/\" target=\"_blank\" rel=\"noreferrer noopener\">receipt scanning<\/a> and medical forms.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"Comparison-Python-VS-No-Code-Tools\">9. Comparison: Python vs. No-Code Tools<\/h2>\n\n\n\n<p>For the developers reading this: should you build or buy? When deciding <strong>how to extract data from documents<\/strong>, you have two paths.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Path A: The Python DIY Route<\/h3>\n\n\n\n<p>You can use open-source libraries like <code>PyPDF2<\/code>, <code>pdfplumber<\/code>, or <code>Tesseract<\/code>.<\/p>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>pdfplumber<\/summary>\n<pre class=\"wp-block-code\"><code>&lt;pre class=\"wp-block-code\"&gt;&lt;code&gt;\nimport pdfplumber<\/code><\/pre>\n<\/details>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1024\" height=\"1024\" data-src=\"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Visual-comparison-of-using-Python-code-vs-No-Code-API-for-learning-how-to-extract-data-from-documents.jpg\" alt=\"Visual comparison of using Python code vs No-Code API for learning how to extract data from documents\" class=\"wp-image-1881 lazyload\" data-srcset=\"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Visual-comparison-of-using-Python-code-vs-No-Code-API-for-learning-how-to-extract-data-from-documents.jpg 1024w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Visual-comparison-of-using-Python-code-vs-No-Code-API-for-learning-how-to-extract-data-from-documents-300x300.jpg 300w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Visual-comparison-of-using-Python-code-vs-No-Code-API-for-learning-how-to-extract-data-from-documents-150x150.jpg 150w, https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Visual-comparison-of-using-Python-code-vs-No-Code-API-for-learning-how-to-extract-data-from-documents-768x768.jpg 768w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/1024;\" \/><\/figure>\n\n\n\n<h1 class=\"wp-block-heading\">Basic extraction example<\/h1>\n\n\n\n<p>with pdfplumber.open(&#8220;invoice.pdf&#8221;) as pdf: <\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>irst_page = pdf.pages&#91;0]\ntext = first_page.extract_text()\nprint(text)\n&lt;\/code&gt;&lt;\/pre&gt;<\/code><\/pre>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pros:<\/strong> Free, full control.<\/li>\n\n\n\n<li><strong>Cons:<\/strong> You must write code to handle rotation, noise, table borders, and layout changes. Maintaining this for 100+ vendor layouts is a full-time job.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Path B: The API Route (ParserData)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pros:<\/strong> Pre-trained models. Handles rotation, handwriting, and tables out of the box. Setup takes minutes, not months.<\/li>\n\n\n\n<li><strong>Cons:<\/strong> Cost per page (though usually cheaper than developer salaries).<\/li>\n<\/ul>\n\n\n\n<p><strong>Verdict:<\/strong> Use Python for learning <strong>how to extract data from documents<\/strong>. Use APIs for production business workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"Future-Trends-Conclusion\">10. Future Trends &amp; Conclusion<\/h2>\n\n\n\n<p>The answer to <strong>how to extract data from documents<\/strong> is shifting from &#8220;Template Matching&#8221; to &#8220;Generative Understanding.&#8221;<\/p>\n\n\n\n<p>By 2026, we are seeing the rise of &#8220;Zero-Shot Extraction,&#8221; where LLMs can read a document they have never seen before and answer questions like <em>&#8220;What is the termination date?&#8221;<\/em> without any prior training.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Summary Checklist<\/h3>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Audit<\/strong> your files (Structured vs. Unstructured).<\/li>\n\n\n\n<li><strong>Choose<\/strong> an AI-first tool like ParserData.<\/li>\n\n\n\n<li><strong>Define<\/strong> your Schema (what fields you need).<\/li>\n\n\n\n<li><strong>Validate<\/strong> with logic rules.<\/li>\n\n\n\n<li><strong>Integrate<\/strong> via API.<\/li>\n<\/ol>\n\n\n\n<p>Mastering <strong>how to extract data from documents<\/strong> is the key to unlocking the 80% of your business data that is currently going to waste. Stop typing. Start extracting.<\/p>\n\n\n\n<p><strong>Ready to automate?<\/strong> <a href=\"https:\/\/parserdata.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Start your free trial with ParserData<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">How to extract data from documents that are handwritten?<\/h3>\n\n\n\n<p>To extract handwritten data, you must use <strong>Intelligent Document Processing (IDP)<\/strong> tools with specialized ICR (Intelligent Character Recognition) engines trained on neural networks, as standard OCR will fail.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can I use Python to learn how to extract data from documents?<\/h3>\n\n\n\n<p>Yes, Python libraries like <code>PyPDF2<\/code> and <code>Tesseract<\/code> are great for learning <strong>how to extract data from documents<\/strong> for simple projects, but they struggle with complex, multi-page tables compared to enterprise AI APIs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What is the most accurate way to extract data?<\/h3>\n\n\n\n<p>The most accurate method is <strong>AI-driven Cognitive Extraction<\/strong> combined with a &#8220;Human-in-the-Loop&#8221; validation step. This combines the speed of machines with human judgment for edge cases.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to extract data from documents securely?<\/h3>\n\n\n\n<p>Ensure your extraction tool is <strong>SOC-2<\/strong> and GDPR compliant. Use APIs that process data in memory without storing it permanently, especially for sensitive financial records.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to extract data from PDF tables?<\/h3>\n\n\n\n<p>Extracting tables requires tools that support <strong>Table Parsing<\/strong>. These tools analyze the grid structure and whitespace to convert PDF rows into structured JSON or CSV arrays automatically.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Recommended<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/parserdata.com\/blog\/types-of-business-documents-to-automate\/\" target=\"_blank\" rel=\"noreferrer noopener\">25 Types of Business Documents to Automate<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/parserdata.com\/blog\/why-integrate-data-analytics\/\" target=\"_blank\" rel=\"noreferrer noopener\">Why Integrate Data Analytics? The 2026 Explainer<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/parserdata.com\/blog\/what-is-report-generation\/\" target=\"_blank\" rel=\"noreferrer noopener\">What Is Report Generation? The Ultimate Guide<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/parserdata.com\/blog\/types-of-financial-data-extraction\/\" target=\"_blank\" rel=\"noreferrer noopener\">7 Types of Financial Data Extraction<\/a><\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"has-small-font-size\">Disclaimer: All comparisons in this article are based on publicly available information and our own product research as of the date of publication. Features, pricing, and capabilities may change over time.<\/p>\n\n\n<p><script type=\"application\/ld+json\" class=\"rank-math-schema\"><br \/>\n{<br \/>\n    \"@context\": \"https:\/\/schema.org\",<br \/>\n    \"@graph\": [<br \/>\n        {<br \/>\n            \"@type\": [\"Person\", \"Organization\"],<br \/>\n            \"@id\": \"https:\/\/parserdata.com\/blog\/#person\",<br \/>\n            \"name\": \"Financial Data Extractor\"<br \/>\n        },<br \/>\n        {<br \/>\n            \"@type\": \"WebSite\",<br \/>\n            \"@id\": \"https:\/\/parserdata.com\/blog\/#website\",<br \/>\n            \"url\": \"https:\/\/parserdata.com\/blog\",<br \/>\n            \"name\": \"Financial Data Extractor\",<br \/>\n            \"publisher\": { \"@id\": \"https:\/\/parserdata.com\/blog\/#person\" },<br \/>\n            \"inLanguage\": \"en-GB\"<br \/>\n        },<br \/>\n        {<br \/>\n            \"@type\": \"ImageObject\",<br \/>\n            \"@id\": \"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Main-guide-image-showing-how-to-extract-data-from-documents-using-AI-technology.jpg\",<br \/>\n            \"url\": \"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Main-guide-image-showing-how-to-extract-data-from-documents-using-AI-technology.jpg\",<br \/>\n            \"width\": \"1200\",<br \/>\n            \"height\": \"675\",<br \/>\n            \"caption\": \"Main guide image showing how to extract data from documents using AI technology\",<br \/>\n            \"inLanguage\": \"en-GB\"<br \/>\n        },<br \/>\n        {<br \/>\n            \"@type\": \"WebPage\",<br \/>\n            \"@id\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#webpage\",<br \/>\n            \"url\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\",<br \/>\n            \"name\": \"How to Extract Data from Documents: The Ultimate 2026 Guide\",<br \/>\n            \"datePublished\": \"2026-02-25T09:00:00+02:00\",<br \/>\n            \"dateModified\": \"2026-02-25T09:00:00+02:00\",<br \/>\n            \"isPartOf\": { \"@id\": \"https:\/\/parserdata.com\/blog\/#website\" },<br \/>\n            \"primaryImageOfPage\": { \"@id\": \"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Main-guide-image-showing-how-to-extract-data-from-documents-using-AI-technology.jpg\" },<br \/>\n            \"inLanguage\": \"en-GB\"<br \/>\n        },<br \/>\n        {<br \/>\n            \"@type\": \"Article\",<br \/>\n            \"headline\": \"How to Extract Data from Documents: The Ultimate 2026 Guide\",<br \/>\n            \"keywords\": \"how to extract data from documents\",<br \/>\n            \"datePublished\": \"2026-02-25T09:00:00+02:00\",<br \/>\n            \"dateModified\": \"2026-02-25T09:00:00+02:00\",<br \/>\n            \"articleSection\": \"Productivity Tips\",<br \/>\n            \"author\": { \"@id\": \"https:\/\/parserdata.com\/blog\/author\/parserdata\/\", \"name\": \"parserdata\" },<br \/>\n            \"publisher\": { \"@id\": \"https:\/\/parserdata.com\/blog\/#person\" },<br \/>\n            \"description\": \"Learn exactly how to extract data from documents efficiently. From simple OCR to AI-driven automation, discover the 5-step process to unlock your data.\",<br \/>\n            \"name\": \"How to Extract Data from Documents: The Ultimate 2026 Guide\",<br \/>\n            \"@id\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#richSnippet\",<br \/>\n            \"isPartOf\": { \"@id\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#webpage\" },<br \/>\n            \"image\": { \"@id\": \"https:\/\/parserdata.com\/blog\/wp-content\/uploads\/2026\/02\/Main-guide-image-showing-how-to-extract-data-from-documents-using-AI-technology.jpg\" },<br \/>\n            \"inLanguage\": \"en-GB\",<br \/>\n            \"mainEntityOfPage\": { \"@id\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#webpage\" }<br \/>\n        },<br \/>\n        {<br \/>\n            \"@type\": \"HowTo\",<br \/>\n            \"name\": \"How to Extract Data from Documents in 5 Steps\",<br \/>\n            \"description\": \"A step-by-step workflow to transform unstructured files into structured data using modern AI tools.\",<br \/>\n            \"totalTime\": \"PT30M\",<br \/>\n            \"step\": [<br \/>\n                {<br \/>\n                    \"@type\": \"HowToStep\",<br \/>\n                    \"name\": \"Audit Your Document Sources\",<br \/>\n                    \"text\": \"Identify if your files are native PDFs, scanned images, or emails to choose the right extraction method.\",<br \/>\n                    \"url\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#step-1-audit\"<br \/>\n                },<br \/>\n                {<br \/>\n                    \"@type\": \"HowToStep\",<br \/>\n                    \"name\": \"Select the Right Extraction Technology\",<br \/>\n                    \"text\": \"Choose between Zonal OCR for fixed forms or Cognitive AI for variable layouts.\",<br \/>\n                    \"url\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#step-2-select-tech\"<br \/>\n                },<br \/>\n                {<br \/>\n                    \"@type\": \"HowToStep\",<br \/>\n                    \"name\": \"Configure the Extraction Schema\",<br \/>\n                    \"text\": \"Define the key-value pairs (e.g., Invoice Number, Date) you need to capture.\",<br \/>\n                    \"url\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#step-3-schema\"<br \/>\n                },<br \/>\n                {<br \/>\n                    \"@type\": \"HowToStep\",<br \/>\n                    \"name\": \"Validate the Output\",<br \/>\n                    \"text\": \"Set up automated rules to check data accuracy (e.g., Math Checks).\",<br \/>\n                    \"url\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#step-4-validate\"<br \/>\n                },<br \/>\n                {<br \/>\n                    \"@type\": \"HowToStep\",<br \/>\n                    \"name\": \"Integrate via API\",<br \/>\n                    \"text\": \"Connect the data stream to your ERP or Database for real-time usage.\",<br \/>\n                    \"url\": \"https:\/\/parserdata.com\/blog\/how-to-extract-data-from-documents\/#step-5-integrate\"<br \/>\n                }<br \/>\n            ]<br \/>\n        },<br \/>\n        {<br \/>\n            \"@type\": \"FAQPage\",<br \/>\n            \"mainEntity\": [<br \/>\n                {<br \/>\n                    \"@type\": \"Question\",<br \/>\n                    \"name\": \"How to extract data from documents that are handwritten?\",<br \/>\n                    \"acceptedAnswer\": {<br \/>\n                        \"@type\": \"Answer\",<br \/>\n                        \"text\": \"To extract handwritten data, you must use Intelligent Document Processing (IDP) tools with specialized ICR (Intelligent Character Recognition) engines trained on neural networks, as standard OCR will fail.\"<br \/>\n                    }<br \/>\n                },<br \/>\n                {<br \/>\n                    \"@type\": \"Question\",<br \/>\n                    \"name\": \"Can I use Python to learn how to extract data from documents?\",<br \/>\n                    \"acceptedAnswer\": {<br \/>\n                        \"@type\": \"Answer\",<br \/>\n                        \"text\": \"Yes, Python libraries like PyPDF2 and Tesseract are great for learning how to extract data from documents for simple projects, but they struggle with complex, multi-page tables compared to AI APIs.\"<br \/>\n                    }<br \/>\n                },<br \/>\n                {<br \/>\n                    \"@type\": \"Question\",<br \/>\n                    \"name\": \"What is the most accurate way to extract data?\",<br \/>\n                    \"acceptedAnswer\": {<br \/>\n                        \"@type\": \"Answer\",<br \/>\n                        \"text\": \"The most accurate method is AI-driven Cognitive Extraction with a 'Human-in-the-Loop' validation step. This combines the speed of machines with human judgment for edge cases.\"<br \/>\n                    }<br \/>\n                },<br \/>\n                {<br \/>\n                    \"@type\": \"Question\",<br \/>\n                    \"name\": \"How to extract data from documents securely?\",<br \/>\n                    \"acceptedAnswer\": {<br \/>\n                        \"@type\": \"Answer\",<br \/>\n                        \"text\": \"Ensure your extraction tool is SOC-2 and GDPR compliant. Use APIs that process data in memory without storing it permanently, especially for financial records.\"<br \/>\n                    }<br \/>\n                },<br \/>\n                {<br \/>\n                    \"@type\": \"Question\",<br \/>\n                    \"name\": \"How to extract data from PDF tables?\",<br \/>\n                    \"acceptedAnswer\": {<br \/>\n                        \"@type\": \"Answer\",<br \/>\n                        \"text\": \"Extracting tables requires tools that support 'Table Parsing'. These tools analyze the grid structure and whitespace to convert PDF rows into structured JSON or CSV arrays.\"<br \/>\n                    }<br \/>\n                }<br \/>\n            ]<br \/>\n        }<br \/>\n    ]<br \/>\n}<br \/>\n<\/script><\/p>","protected":false},"excerpt":{"rendered":"<p>Every day, businesses generate 2.5 quintillion bytes of data. Yet, a massive portion of this value is trapped in unstructured formats: invoices, contracts, resumes, and forms. For modern organizations, the ability to unlock this information is not just a technical skill it is a competitive necessity. This brings us to the critical question: how to&#8230;<\/p>\n","protected":false},"author":1,"featured_media":1889,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_swpsp_post_exclude":false,"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"left","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[5],"tags":[168,154,164,85],"class_list":["post-1879","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-productivity-tips","tag-ai-data-extraction","tag-automated-extraction-en","tag-automating-data-extraction-en","tag-data-extraction-en"],"_links":{"self":[{"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/posts\/1879","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/comments?post=1879"}],"version-history":[{"count":26,"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/posts\/1879\/revisions"}],"predecessor-version":[{"id":2183,"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/posts\/1879\/revisions\/2183"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/media\/1889"}],"wp:attachment":[{"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/media?parent=1879"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/categories?post=1879"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/parserdata.com\/blog\/wp-json\/wp\/v2\/tags?post=1879"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}