{"id":4559,"date":"2026-06-03T11:00:00","date_gmt":"2026-06-03T11:00:00","guid":{"rendered":"https:\/\/vananservices.com\/blog\/?p=4559"},"modified":"2026-06-01T12:50:42","modified_gmt":"2026-06-01T12:50:42","slug":"the-accuracy-gap-why-ai-typing-tools-fail-on-handw","status":"publish","type":"post","link":"https:\/\/vananservices.com\/blog\/the-accuracy-gap-why-ai-typing-tools-fail-on-handw\/","title":{"rendered":"The Accuracy Gap: Why AI Typing Tools Fail on Handwritten Legal Documents"},"content":{"rendered":"<p>Handwritten legal documents still play a major role in law firms, courts, archives, and government offices.\u000bBut many AI typing tools struggle to convert these documents into clean, searchable text.\u000bThis creates a serious <em>accuracy gap<\/em> that can lead to legal mistakes, compliance issues, and lost time.<\/p>\n<p>Legal professionals need more than fast transcription.\u000bThey need precision, consistency, and trust.\u000bThat is where many modern AI handwriting recognition tools still fall short.<\/p>\n<p>[toc]<\/p>\n<h3 id=\"key-takeaways\"><strong>Key Takeaways<br \/>\n<\/strong><\/h3>\n<ul>\n<li>AI typing tools often fail when processing handwritten legal documents.<\/li>\n<li>Poor handwriting recognition can create legal and compliance risks.<\/li>\n<li>Historical legal records are especially difficult for AI systems.<\/li>\n<li>Formatting, annotations, signatures, and legal terminology reduce AI accuracy.<\/li>\n<li>Human review is still essential for legal document transcription.<\/li>\n<li>Hybrid AI + human workflows provide the best results for law firms and archivists.<\/li>\n<\/ul>\n<h3 id=\"why-handwritten-legal-documents-are-still-common\"><strong>Why Handwritten Legal Documents Are Still Common<br \/>\n<\/strong><\/h3>\n<p>Despite digital transformation, handwritten legal records remain everywhere.<\/p>\n<p>Many legal systems still rely on:<\/p>\n<ul>\n<li>Signed contracts<\/li>\n<li>Court notes<\/li>\n<li>Affidavits<\/li>\n<li>Wills and trusts<\/li>\n<li>Property records<\/li>\n<li>Historical archives<\/li>\n<li>Intake forms<\/li>\n<li>Attorney notes<\/li>\n<\/ul>\n<p>Older legal institutions often store decades of paper files.\u000bSome records were written before digital systems even existed.<\/p>\n<p>Even today, lawyers frequently annotate printed documents by hand.\u000bJudges may also add handwritten notes during hearings or reviews.<\/p>\n<p>Because of this, firms increasingly use AI typing tools to digitize information quickly.<\/p>\n<p>The problem is accuracy.<\/p>\n<h3 id=\"the-promise-of-ai-typing-tools\"><strong>The Promise of AI Typing Tools<br \/>\n<\/strong><\/h3>\n<p>AI-powered OCR (Optical Character Recognition) systems claim to automate document transcription.\u000bMany platforms advertise fast conversion from handwriting to editable text.<\/p>\n<p>Popular AI typing technologies include:<\/p>\n<table style=\"width: 100%; border-collapse: collapse; margin: 16px 0;\">\n<tbody>\n<tr>\n<th style=\"border: 1px solid #ddd; padding: 8px; background: #f5f5f5; font-weight: 600;\"><strong>Technology<\/strong><\/th>\n<th style=\"border: 1px solid #ddd; padding: 8px; background: #f5f5f5; font-weight: 600;\"><strong>Purpose<\/strong><\/th>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">OCR<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Reads printed text<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">ICR (Intelligent Character Recognition)<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Attempts to read handwriting<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">NLP (Natural Language Processing)<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Understands language context<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Machine Learning Models<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Improve recognition over time<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>These systems work reasonably well for:<\/p>\n<ul>\n<li>Typed contracts<\/li>\n<li>Printed invoices<\/li>\n<li>Standard forms<\/li>\n<li>Clear block handwriting<\/li>\n<\/ul>\n<p>However, legal handwriting presents a much harder challenge.<\/p>\n<h3 id=\"why-ai-struggles-with-handwritten-legal-documents\"><strong>Why AI Struggles With Handwritten Legal Documents<br \/>\n<\/strong><\/h3>\n<h4 id=\"inconsistent-handwriting-styles\"><strong>Inconsistent Handwriting Styles<br \/>\n<\/strong><\/h4>\n<p>Every person writes differently.\u000bSome attorneys use cursive.\u000bOthers use shorthand or compressed notes.<\/p>\n<p>AI systems depend on pattern recognition.\u000bWhen handwriting varies too much, accuracy drops sharply.<\/p>\n<p>Legal documents often contain:<\/p>\n<ul>\n<li>Fast handwritten notes<\/li>\n<li>Marginal comments<\/li>\n<li>Crossed-out sections<\/li>\n<li>Initials<\/li>\n<li>Symbols<\/li>\n<li>Abbreviations<\/li>\n<\/ul>\n<p>These patterns confuse AI models.<\/p>\n<p>A single misread word can completely change legal meaning.<\/p>\n<p>For example:<\/p>\n<ul>\n<li>\u201cgrantor\u201d vs \u201cgrantee\u201d<\/li>\n<li>\u201cshall\u201d vs \u201cshall not\u201d<\/li>\n<li>\u201cliable\u201d vs \u201cnot liable\u201d<\/li>\n<\/ul>\n<p>Small errors create major legal consequences.<\/p>\n<h3 id=\"legal-terminology-creates-additional-problems\"><strong>Legal Terminology Creates Additional Problems<br \/>\n<\/strong><\/h3>\n<p>Legal language is highly specialized.<\/p>\n<p>AI typing systems trained on general handwriting datasets may not understand:<\/p>\n<ul>\n<li>Latin legal terms<\/li>\n<li>Case citations<\/li>\n<li>Statutory references<\/li>\n<li>Jurisdiction abbreviations<\/li>\n<li>Legal shorthand<\/li>\n<\/ul>\n<p>Terms like <em>habeas corpus<\/em> or <em>res judicata<\/em> are uncommon in normal datasets.<\/p>\n<p>This increases transcription errors significantly.<\/p>\n<p>Many legal records also contain industry-specific language related to:<\/p>\n<ul>\n<li>Real estate<\/li>\n<li>Probate<\/li>\n<li>Corporate law<\/li>\n<li>Litigation<\/li>\n<li>Intellectual property<\/li>\n<\/ul>\n<p>Without domain-specific training, AI tools struggle to interpret context correctly.<\/p>\n<h3 id=\"historical-legal-documents-are-extremely-difficult\"><strong>Historical Legal Documents Are Extremely Difficult<br \/>\n<\/strong><\/h3>\n<p>Document archivists face even bigger challenges.<\/p>\n<p>Older legal records often include:<\/p>\n<ul>\n<li>Faded ink<\/li>\n<li>Water damage<\/li>\n<li>Stains<\/li>\n<li>Torn pages<\/li>\n<li>Obsolete handwriting styles<\/li>\n<li>Non-standard spelling<\/li>\n<\/ul>\n<p>Historical court documents may also use handwriting styles no longer taught today.<\/p>\n<p>AI systems trained on modern writing samples frequently fail on archival materials.<\/p>\n<p>This creates major digitization barriers for:<\/p>\n<ul>\n<li>Courts<\/li>\n<li>Universities<\/li>\n<li>Government archives<\/li>\n<li>Historical societies<\/li>\n<\/ul>\n<p>Preserving legal history requires extremely high transcription accuracy.<\/p>\n<h3 id=\"signatures-and-annotations-reduce-ocr-accuracy\"><strong>Signatures and Annotations Reduce OCR Accuracy<br \/>\n<\/strong><\/h3>\n<p>Handwritten legal documents rarely contain clean layouts.<\/p>\n<p>Many include:<\/p>\n<ul>\n<li>Signatures<\/li>\n<li>Stamps<\/li>\n<li>Seals<\/li>\n<li>Highlighting<\/li>\n<li>Side notes<\/li>\n<li>Multi-column formatting<\/li>\n<\/ul>\n<p>These elements interfere with AI recognition engines.<\/p>\n<p>For example, a handwritten note in the margin may overlap typed text.\u000bOCR software may merge both sections incorrectly.<\/p>\n<p>This creates unreadable outputs and formatting corruption.<\/p>\n<p>Legal formatting matters.\u000bEven paragraph placement can affect interpretation.<\/p>\n<h3 id=\"compliance-and-liability-risks\"><strong>Compliance and Liability Risks<br \/>\n<\/strong><\/h3>\n<p>Poor transcription accuracy creates serious legal exposure.<\/p>\n<p>A transcription error can lead to:<\/p>\n<ul>\n<li>Contract disputes<\/li>\n<li>Filing mistakes<\/li>\n<li>Discovery issues<\/li>\n<li>Compliance violations<\/li>\n<li>Financial penalties<\/li>\n<\/ul>\n<p>Law firms cannot rely on \u201cmostly accurate\u201d systems.<\/p>\n<p>Legal professionals need near-perfect precision.<\/p>\n<p>This is especially important for:<\/p>\n<table style=\"width: 100%; border-collapse: collapse; margin: 16px 0;\">\n<tbody>\n<tr>\n<th style=\"border: 1px solid #ddd; padding: 8px; background: #f5f5f5; font-weight: 600;\"><strong>Legal Area<\/strong><\/th>\n<th style=\"border: 1px solid #ddd; padding: 8px; background: #f5f5f5; font-weight: 600;\"><strong>Risk Level<\/strong><\/th>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Litigation<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Extremely High<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Real Estate<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">High<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Probate<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">High<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Compliance<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Very High<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Contract Law<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Extremely High<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Even small transcription mistakes may become evidence problems in court.<\/p>\n<p>That is why human oversight remains essential.<\/p>\n<h3 id=\"ai-bias-and-training-limitations\"><strong>AI Bias and Training Limitations<br \/>\n<\/strong><\/h3>\n<p>Many AI handwriting systems are trained on narrow datasets.<\/p>\n<p>This creates recognition bias.<\/p>\n<p>Some models perform better with:<\/p>\n<ul>\n<li>Certain writing styles<\/li>\n<li>Specific languages<\/li>\n<li>Modern handwriting<\/li>\n<li>Younger demographics<\/li>\n<\/ul>\n<p>Older handwriting styles often produce lower accuracy rates.<\/p>\n<p>Legal institutions dealing with diverse records may experience inconsistent results across cases.<\/p>\n<p>Training data quality directly impacts performance.<\/p>\n<p>Unfortunately, many commercial AI vendors do not disclose:<\/p>\n<ul>\n<li>Dataset size<\/li>\n<li>Accuracy benchmarks<\/li>\n<li>Legal-specific testing<\/li>\n<li>Error rates by handwriting type<\/li>\n<\/ul>\n<p>This lack of transparency creates trust concerns.<\/p>\n<h3 id=\"why-human-review-is-still-necessary\"><strong>Why Human Review Is Still Necessary<br \/>\n<\/strong><\/h3>\n<p>AI transcription should not replace legal review.<\/p>\n<p>Instead, it should support legal workflows.<\/p>\n<p>The most reliable process combines:<\/p>\n<ul>\n<li>AI-powered initial transcription<\/li>\n<li>Human proofreading<\/li>\n<li>Legal verification<\/li>\n<li>Quality assurance checks<\/li>\n<\/ul>\n<p>This hybrid approach improves both speed and accuracy.<\/p>\n<p>Paralegals and legal assistants still play a critical role in validating content.<\/p>\n<p>Human reviewers can understand:<\/p>\n<ul>\n<li>Context<\/li>\n<li>Intent<\/li>\n<li>Legal nuance<\/li>\n<li>Ambiguous handwriting<\/li>\n<\/ul>\n<p>AI cannot fully replicate this judgment yet.<\/p>\n<h3 id=\"the-best-use-cases-for-ai-in-legal-transcription\"><strong>The Best Use Cases for AI in Legal Transcription<br \/>\n<\/strong><\/h3>\n<p>AI typing tools still provide value when used correctly.<\/p>\n<p>They work best for:<\/p>\n<h4 id=\"bulk-document-sorting\"><strong>Bulk Document Sorting<br \/>\n<\/strong><\/h4>\n<p>AI can categorize thousands of files quickly.<\/p>\n<p>Examples include:<\/p>\n<ul>\n<li>Case types<\/li>\n<li>Dates<\/li>\n<li>Client names<\/li>\n<li>Document categories<\/li>\n<\/ul>\n<h4 id=\"searchable-archives\"><strong>Searchable Archives<br \/>\n<\/strong><\/h4>\n<p>Even imperfect OCR can improve archive searchability.<\/p>\n<p>Partial indexing helps researchers locate documents faster.<\/p>\n<h4 id=\"draft-transcription\"><strong>Draft Transcription<br \/>\n<\/strong><\/h4>\n<p>AI can create rough drafts for human editors to review.<\/p>\n<p>This reduces manual typing time.<\/p>\n<h4 id=\"metadata-extraction\"><strong>Metadata Extraction<br \/>\n<\/strong><\/h4>\n<p>Some systems accurately pull:<\/p>\n<ul>\n<li>Dates<\/li>\n<li>Addresses<\/li>\n<li>Names<\/li>\n<li>Case numbers<\/li>\n<\/ul>\n<p>Structured fields are easier for AI to identify.<\/p>\n<h3 id=\"emerging-improvements-in-ai-handwriting-recognitio\"><strong>Emerging Improvements in AI Handwriting Recognition<br \/>\n<\/strong><\/h3>\n<p>AI handwriting recognition is improving rapidly.<\/p>\n<p>New developments include:<\/p>\n<ul>\n<li>Transformer-based language models<\/li>\n<li>Legal-specific AI training<\/li>\n<li>Context-aware OCR<\/li>\n<li>Multi-language recognition<\/li>\n<li>Adaptive learning systems<\/li>\n<\/ul>\n<p>Some vendors now train models specifically on legal datasets.<\/p>\n<p>This improves recognition for:<\/p>\n<ul>\n<li>Court terminology<\/li>\n<li>Legal forms<\/li>\n<li>Structured filings<\/li>\n<li>Historical records<\/li>\n<\/ul>\n<p>However, even advanced systems still require verification.<\/p>\n<p>Accuracy expectations in law remain exceptionally high.<\/p>\n<h3 id=\"how-law-firms-can-reduce-ai-transcription-errors\"><strong>How Law Firms Can Reduce AI Transcription Errors<br \/>\n<\/strong><\/h3>\n<h4 id=\"choose-legal-specific-ai-tools\"><strong>Choose Legal-Specific AI Tools<br \/>\n<\/strong><\/h4>\n<p>Generic OCR software may not meet legal standards.<\/p>\n<p>Look for solutions trained on legal documents specifically.<\/p>\n<h4 id=\"use-high-quality-scans\"><strong>Use High-Quality Scans<br \/>\n<\/strong><\/h4>\n<p>Better scans improve OCR accuracy dramatically.<\/p>\n<p>Recommended standards include:<\/p>\n<table style=\"width: 100%; border-collapse: collapse; margin: 16px 0;\">\n<tbody>\n<tr>\n<th style=\"border: 1px solid #ddd; padding: 8px; background: #f5f5f5; font-weight: 600;\"><strong>Scan Feature<\/strong><\/th>\n<th style=\"border: 1px solid #ddd; padding: 8px; background: #f5f5f5; font-weight: 600;\"><strong>Recommendation<\/strong><\/th>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Resolution<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">300 DPI minimum<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">File Format<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">TIFF or PDF<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Color Mode<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Grayscale preferred<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Skew Correction<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Enabled<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4 id=\"implement-human-qa-workflows\"><strong>Implement Human QA Workflows<br \/>\n<\/strong><\/h4>\n<p>Always review AI-generated transcripts manually.<\/p>\n<p>Quality assurance should include:<\/p>\n<ul>\n<li>Double-checking names<\/li>\n<li>Reviewing legal terminology<\/li>\n<li>Verifying dates<\/li>\n<li>Comparing originals<\/li>\n<\/ul>\n<h4 id=\"create-internal-accuracy-policies\"><strong>Create Internal Accuracy Policies<br \/>\n<\/strong><\/h4>\n<p>Law firms should define acceptable transcription standards.<\/p>\n<p>This helps reduce operational risk.<\/p>\n<h3 id=\"the-future-of-ai-in-legal-document-processing\"><strong>The Future of AI in Legal Document Processing<br \/>\n<\/strong><\/h3>\n<p>AI will continue transforming legal operations.<\/p>\n<p>But handwritten document recognition remains one of the hardest problems in legal technology.<\/p>\n<p>Future systems may eventually achieve:<\/p>\n<ul>\n<li>Better contextual understanding<\/li>\n<li>Higher handwriting accuracy<\/li>\n<li>Improved legal reasoning<\/li>\n<li>Real-time verification<\/li>\n<\/ul>\n<p>Still, trust in legal documentation depends on precision.<\/p>\n<p>For now, AI works best as an assistant \u2014 not a replacement for legal expertise.<\/p>\n<p>Law firms, paralegals, and archivists should approach AI transcription strategically.<\/p>\n<p>Automation saves time.\u000bBut accuracy protects legal integrity.<\/p>\n<h3 id=\"faqs\"><strong>FAQs<br \/>\n<\/strong><\/h3>\n<h4 id=\"why-do-ai-typing-tools-struggle-with-handwritten-l\"><strong>Why do AI typing tools struggle with handwritten legal documents?<br \/>\n<\/strong><\/h4>\n<p>AI systems struggle because handwriting varies widely between individuals.\u000bLegal documents also contain specialized terminology, annotations, and formatting that confuse recognition models.<\/p>\n<h4 id=\"are-ocr-tools-accurate-enough-for-legal-work\"><strong>Are OCR tools accurate enough for legal work?<br \/>\n<\/strong><\/h4>\n<p>OCR tools can help with initial transcription and indexing.\u000bHowever, human review is still necessary for legal accuracy and compliance.<\/p>\n<h4 id=\"what-is-the-difference-between-ocr-and-icr\"><strong>What is the difference between OCR and ICR?<br \/>\n<\/strong><\/h4>\n<p>OCR reads printed text.\u000bICR is designed to recognize handwritten characters using AI and machine learning.<\/p>\n<h4 id=\"can-ai-transcribe-historical-legal-records-accurat\"><strong>Can AI transcribe historical legal records accurately?<br \/>\n<\/strong><\/h4>\n<p>Accuracy varies significantly.\u000bHistorical documents with faded ink, cursive writing, or damage remain difficult for most AI systems.<\/p>\n<h4 id=\"should-law-firms-trust-ai-transcription-completely\"><strong>Should law firms trust AI transcription completely?<br \/>\n<\/strong><\/h4>\n<p>No.\u000bAI should support legal workflows, not replace professional legal review and verification.<\/p>\n<h3 id=\"final-thoughts\"><strong>Final Thoughts<br \/>\n<\/strong><\/h3>\n<p>The legal industry depends on accuracy, context, and trust.\u000bWhile AI typing tools continue improving, handwritten legal documents remain a major challenge.<\/p>\n<p>The gap between automation and legal-grade precision is still significant.<\/p>\n<p>Organizations that combine AI efficiency with human expertise will achieve the best results while minimizing legal risk.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Handwritten legal documents still play a major role in law firms, courts, archives, and government&hellip;<\/p>\n","protected":false},"author":1,"featured_media":4558,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[490,1304],"tags":[],"ppma_author":[583],"class_list":["post-4559","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-typing-services-new-york","category-typing-services"],"authors":[{"term_id":583,"user_id":1,"is_guest":0,"slug":"vanan-wordpress-user","display_name":"Kayla Vega","avatar_url":{"url":"https:\/\/vananservices.com\/blog\/wp-content\/uploads\/2025\/12\/1711561174327.jpg","url2x":"https:\/\/vananservices.com\/blog\/wp-content\/uploads\/2025\/12\/1711561174327.jpg"},"author_category":"1","first_name":"Kayla","last_name":"Vega","user_url":"https:\/\/vananservices.com\/blog","job_title":"","description":"<strong>Kayla Vega<\/strong> is a seasoned content marketing specialist with over a decade of experience in the translation and localization industry. Passionate about bridging cultural and linguistic gaps, she has honed her expertise in creating impactful content that resonates across global audiences. With a keen eye for SEO and trends in the linguistic tech sector, Kayla specializes in delivering content that simplifies complex concepts in translation technology, AI-driven services, and cross-cultural communication. When she's not writing, Kayla enjoys exploring new hiking trails and volunteering at local community events, balancing her professional life with her personal commitment to helping others."}],"_links":{"self":[{"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/posts\/4559","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/comments?post=4559"}],"version-history":[{"count":1,"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/posts\/4559\/revisions"}],"predecessor-version":[{"id":4580,"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/posts\/4559\/revisions\/4580"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/media\/4558"}],"wp:attachment":[{"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/media?parent=4559"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/categories?post=4559"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/tags?post=4559"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/vananservices.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=4559"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}