{"id":114,"date":"2026-09-09T22:03:27","date_gmt":"2026-09-09T22:03:27","guid":{"rendered":"https:\/\/globaldataentrysolutions.com\/blogs\/?p=114"},"modified":"2026-09-09T22:07:50","modified_gmt":"2026-09-09T22:07:50","slug":"ocr-output-is-not-automatically-clean-data-what-should-happen-after-text-recognition","status":"publish","type":"post","link":"https:\/\/globaldataentrysolutions.com\/blogs\/ocr-output-is-not-automatically-clean-data-what-should-happen-after-text-recognition\/","title":{"rendered":"OCR Output Is Not Automatically Clean Data: What Should Happen After Text Recognition?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"114\" class=\"elementor elementor-114\">\n\t\t\t\t<div class=\"elementor-element elementor-element-095a402 e-con-full e-flex e-con e-parent\" data-id=\"095a402\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7bb0cf0 elementor-widget elementor-widget-html\" data-id=\"7bb0cf0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t\t<article class=\"gdes-blog-article\">\r\n\r\n<style>\r\n.gdes-blog-article{\r\n  max-width:920px;\r\n  margin:0 auto;\r\n  font-family:\"Open Sans\",Arial,sans-serif;\r\n  color:#344454;\r\n  font-size:17px;\r\n  line-height:1.8;\r\n}\r\n.gdes-blog-article h2{\r\n  color:#203248;\r\n  font-size:30px;\r\n  line-height:1.3;\r\n  margin:48px 0 18px;\r\n  font-weight:700;\r\n}\r\n.gdes-blog-article h3{\r\n  color:#203248;\r\n  font-size:22px;\r\n  margin:32px 0 12px;\r\n  font-weight:700;\r\n}\r\n.gdes-blog-article p{margin:0 0 20px}\r\n.gdes-blog-article a{\r\n  color:#168cc2;\r\n  font-weight:600;\r\n  text-decoration:none;\r\n}\r\n.gdes-blog-article a:hover{text-decoration:underline}\r\n.gdes-blog-intro{\r\n  font-size:19px;\r\n  color:#526474;\r\n}\r\n.gdes-blog-highlight{\r\n  background:#f4f8fb;\r\n  border-left:4px solid #27aae1;\r\n  padding:24px 28px;\r\n  margin:30px 0;\r\n  border-radius:4px;\r\n}\r\n.gdes-blog-list{\r\n  padding-left:22px;\r\n  margin:15px 0 26px;\r\n}\r\n.gdes-blog-list li{margin-bottom:10px}\r\n.gdes-blog-process{\r\n  background:#f6f8fa;\r\n  border:1px solid #e3e9ee;\r\n  border-radius:7px;\r\n  padding:28px;\r\n  margin:30px 0;\r\n}\r\n.gdes-blog-process-step{\r\n  padding:14px 0;\r\n  border-bottom:1px solid #dfe5ea;\r\n}\r\n.gdes-blog-process-step:last-child{border-bottom:none}\r\n.gdes-blog-process-step strong{color:#203248}\r\n.gdes-blog-table-wrap{\r\n  overflow-x:auto;\r\n  margin:30px 0;\r\n}\r\n.gdes-blog-table{\r\n  width:100%;\r\n  border-collapse:collapse;\r\n  font-size:15px;\r\n}\r\n.gdes-blog-table th{\r\n  background:#203248;\r\n  color:#fff;\r\n  text-align:left;\r\n  padding:14px;\r\n}\r\n.gdes-blog-table td{\r\n  border:1px solid #dde4e9;\r\n  padding:14px;\r\n  vertical-align:top;\r\n}\r\n.gdes-blog-faq{\r\n  border:1px solid #e1e7eb;\r\n  border-radius:6px;\r\n  padding:22px 25px;\r\n  margin-bottom:16px;\r\n}\r\n.gdes-blog-faq h3{\r\n  margin:0 0 10px;\r\n  font-size:19px;\r\n}\r\n.gdes-blog-faq p{margin:0}\r\n.gdes-blog-cta{\r\n  background:#203248;\r\n  padding:38px 32px;\r\n  margin:45px 0 10px;\r\n  border-radius:7px;\r\n  color:#fff;\r\n  text-align:center;\r\n}\r\n.gdes-blog-cta h2{\r\n  color:#fff;\r\n  margin:0 0 15px;\r\n}\r\n.gdes-blog-cta p{\r\n  color:#dbe4ec;\r\n  max-width:720px;\r\n  margin:0 auto 22px;\r\n}\r\n.gdes-blog-btn{\r\n  display:inline-block;\r\n  background:#f7941d;\r\n  color:#fff!important;\r\n  padding:12px 24px;\r\n  border-radius:4px;\r\n  font-weight:700!important;\r\n}\r\n@media(max-width:767px){\r\n  .gdes-blog-article{font-size:16px}\r\n  .gdes-blog-article h2{font-size:25px}\r\n  .gdes-blog-article h3{font-size:20px}\r\n}\r\n<\/style>\r\n\r\n<p class=\"gdes-blog-intro\">\r\nOCR can convert scanned or image-based text into machine-readable content, but recognized text is not automatically the same as clean, validated or ready-to-use data.\r\n<\/p>\r\n\r\n<p>\r\nThe quality of OCR output depends heavily on the source image, document layout, font quality, handwriting, scan clarity and the structure expected in the final dataset.\r\n<\/p>\r\n\r\n<p>\r\nThat is why OCR should often be treated as one stage in a broader workflow rather than the final output.\r\n<\/p>\r\n\r\n<h2>Text Recognition and Data Readiness Are Different Things<\/h2>\r\n\r\n<p>\r\nOCR answers the question:\r\n<\/p>\r\n\r\n<div class=\"gdes-blog-highlight\">\r\n<strong>\u201cWhat text can be recognized from this image or scanned document?\u201d<\/strong>\r\n<\/div>\r\n\r\n<p>\r\nA business-data workflow must answer additional questions:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Was the text recognized correctly?<\/li>\r\n<li>Did the right field receive the right value?<\/li>\r\n<li>Was the document classified correctly?<\/li>\r\n<li>Were tables preserved properly?<\/li>\r\n<li>Were missing or unreadable values identified?<\/li>\r\n<li>Can the result be traced back to the source?<\/li>\r\n<li>Is the workload fully reconciled?<\/li>\r\n<\/ul>\r\n\r\n<h2>1. Start With Source Quality<\/h2>\r\n\r\n<p>\r\nOCR performance is strongly influenced by the quality of the source image.\r\n<\/p>\r\n\r\n<p>\r\nPotential source issues include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Low-resolution scans<\/li>\r\n<li>Blurred text<\/li>\r\n<li>Skewed pages<\/li>\r\n<li>Faded printing<\/li>\r\n<li>Shadows or background noise<\/li>\r\n<li>Handwritten annotations<\/li>\r\n<li>Pages captured at inconsistent angles<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nWhen source quality is weak, recognized text should receive additional review rather than being treated as final data.\r\n<\/p>\r\n\r\n<h2>2. OCR Should Be Matched to the Document Type<\/h2>\r\n\r\n<p>\r\nDifferent documents create different recognition challenges.\r\n<\/p>\r\n\r\n<p>\r\nExamples include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Forms<\/li>\r\n<li>Invoices<\/li>\r\n<li>Statements<\/li>\r\n<li>Reports<\/li>\r\n<li>Tables<\/li>\r\n<li>Application documents<\/li>\r\n<li>Catalog pages<\/li>\r\n<li>Scanned correspondence<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nThe same OCR output cannot always be processed using the same downstream rules.\r\n<\/p>\r\n\r\n<p>\r\nThat is why\r\n<a href=\"\/blogs\/data-classification-before-data-entry-workflow\/\">record classification<\/a>\r\ncan be useful before structured processing begins.\r\n<\/p>\r\n\r\n<h2>3. Recognized Text Still Needs Field Mapping<\/h2>\r\n\r\n<p>\r\nEven when OCR recognizes words correctly, the workflow still needs to understand where each value belongs.\r\n<\/p>\r\n\r\n<p>\r\nFor example:\r\n<\/p>\r\n\r\n<div class=\"gdes-blog-table-wrap\">\r\n<table class=\"gdes-blog-table\">\r\n<thead>\r\n<tr>\r\n<th>Recognized Source<\/th>\r\n<th>Target Field<\/th>\r\n<th>Review Need<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td>Invoice Number<\/td>\r\n<td>Invoice_ID<\/td>\r\n<td>Confirm full identifier<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Document Date<\/td>\r\n<td>Document_Date<\/td>\r\n<td>Check approved date format<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Company Name<\/td>\r\n<td>Company_Name<\/td>\r\n<td>Confirm against source<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Total Amount<\/td>\r\n<td>Amount<\/td>\r\n<td>Check numeric structure<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n\r\n<p>\r\nOCR extracts or recognizes text. Field mapping turns that text into structured business data.\r\n<\/p>\r\n\r\n<h2>4. Character-Level Errors Can Change Meaning<\/h2>\r\n\r\n<p>\r\nSmall recognition errors can materially change a record.\r\n<\/p>\r\n\r\n<p>\r\nExamples may include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>0 recognized as O<\/li>\r\n<li>1 recognized as I<\/li>\r\n<li>5 recognized as S<\/li>\r\n<li>Decimal points omitted<\/li>\r\n<li>Hyphens lost from identifiers<\/li>\r\n<li>Characters dropped from reference numbers<\/li>\r\n<\/ul>\r\n\r\n<div class=\"gdes-blog-highlight\">\r\n<strong>A value can look visually plausible and still be wrong.<\/strong>\r\n<\/div>\r\n\r\n<p>\r\nFields such as identifiers, reference numbers, amounts and dates therefore may need specific validation rules.\r\n<\/p>\r\n\r\n<h2>5. Tables Require Structural Review<\/h2>\r\n\r\n<p>\r\nOCR may recognize the individual words inside a table while still losing the relationship between rows and columns.\r\n<\/p>\r\n\r\n<p>\r\nPotential issues include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Values moving into the wrong column<\/li>\r\n<li>Headers being treated as records<\/li>\r\n<li>Multi-line cells splitting incorrectly<\/li>\r\n<li>Continuation rows becoming separate records<\/li>\r\n<li>Repeated headers appearing inside the data<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nFor tabular workloads, the output should be validated against the intended target structure.\r\n<\/p>\r\n\r\n<h2>6. Handwritten Information Needs Different Expectations<\/h2>\r\n\r\n<p>\r\nHandwritten or constrained handwritten fields may require different recognition and review approaches from machine-printed text.\r\n<\/p>\r\n\r\n<p>\r\nWhere handwriting is unclear, operators should not create a value that the source does not reliably support.\r\n<\/p>\r\n\r\n<p>\r\nOur\r\n<a href=\"\/ocr-and-icr-services\/\">OCR and ICR services<\/a>\r\nsupport document-recognition workflows where printed or constrained handwritten information needs structured review.\r\n<\/p>\r\n\r\n<h2>7. OCR Cleanup Is More Than Spell Checking<\/h2>\r\n\r\n<p>\r\nCleanup can involve several types of corrections and normalization.\r\n<\/p>\r\n\r\n<p>\r\nDepending on the workflow, this may include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Removing recognition artifacts<\/li>\r\n<li>Correcting spacing<\/li>\r\n<li>Restoring field structure<\/li>\r\n<li>Reviewing character substitutions<\/li>\r\n<li>Separating headers from record data<\/li>\r\n<li>Normalizing approved formats<\/li>\r\n<li>Flagging unreadable content<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nFor OCR-specific post-processing, see our\r\n<a href=\"\/ocr-cleanup-processing\/\">OCR cleanup processing services<\/a>.\r\n<\/p>\r\n\r\n<h2>8. Validation Should Compare OCR Output With the Source<\/h2>\r\n\r\n<p>\r\nA clean-looking text file can still contain recognition errors.\r\n<\/p>\r\n\r\n<p>\r\nValidation may compare selected fields back to the image or scanned document.\r\n<\/p>\r\n\r\n<p>\r\nUseful checks can include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Required values<\/li>\r\n<li>Critical identifiers<\/li>\r\n<li>Dates<\/li>\r\n<li>Numeric values<\/li>\r\n<li>Field position<\/li>\r\n<li>Document references<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nThis follows the broader principle described in our\r\n<a href=\"\/blogs\/data-entry-quality-control-workflow\/\">data entry quality control guide<\/a>.\r\n<\/p>\r\n\r\n<h2>9. Exceptions Should Be Created for Unreadable or Uncertain Content<\/h2>\r\n\r\n<p>\r\nOCR does not eliminate uncertainty.\r\n<\/p>\r\n\r\n<p>\r\nSome fields may still be:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Unreadable<\/li>\r\n<li>Partially visible<\/li>\r\n<li>Conflicting<\/li>\r\n<li>Missing<\/li>\r\n<li>Outside the defined field map<\/li>\r\n<li>Ambiguous after recognition<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nThese should move into a controlled review queue rather than being silently converted into a guessed value.\r\n<\/p>\r\n\r\n<div class=\"gdes-blog-highlight\">\r\n<strong>When the source does not support a reliable value, the workflow should preserve the uncertainty instead of hiding it.<\/strong>\r\n<\/div>\r\n\r\n<h2>10. Preserve Source Traceability<\/h2>\r\n\r\n<p>\r\nThe final structured record should remain connected to the image or document that produced it where review requirements call for traceability.\r\n<\/p>\r\n\r\n<p>\r\nPossible references include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Source filename<\/li>\r\n<li>Page number<\/li>\r\n<li>Document ID<\/li>\r\n<li>Batch ID<\/li>\r\n<li>Original record reference<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nSee our related article:\r\n<a href=\"\/blogs\/data-traceability-source-validation-workflow\/\">A Clean Output File Is Not Enough If You Cannot Trace It Back to the Source<\/a>.\r\n<\/p>\r\n\r\n<h2>11. OCR Output May Need Data Cleansing<\/h2>\r\n\r\n<p>\r\nOnce text has been recognized and mapped, the dataset may still contain formatting inconsistencies.\r\n<\/p>\r\n\r\n<p>\r\nExamples include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>Mixed capitalization<\/li>\r\n<li>Inconsistent dates<\/li>\r\n<li>Repeated spaces<\/li>\r\n<li>Duplicate records<\/li>\r\n<li>Inconsistent categories<\/li>\r\n<li>Blank-value differences<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nWhere this is part of the scope,\r\n<a href=\"\/data-cleansing-processing\/\">data cleansing processing<\/a>\r\ncan support normalization before final delivery.\r\n<\/p>\r\n\r\n<h2>12. Reconciliation Confirms the Full Source Population Was Accounted For<\/h2>\r\n\r\n<p>\r\nOCR may successfully recognize most documents while some fail or require review.\r\n<\/p>\r\n\r\n<p>\r\nA controlled workflow should explain the complete population.\r\n<\/p>\r\n\r\n<div class=\"gdes-blog-table-wrap\">\r\n<table class=\"gdes-blog-table\">\r\n<thead>\r\n<tr>\r\n<th>Status<\/th>\r\n<th>Operational Meaning<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td>Received<\/td>\r\n<td>Total documents or pages entering the workflow<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Recognized<\/td>\r\n<td>OCR text generated<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Validated<\/td>\r\n<td>Required checks completed<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Review Required<\/td>\r\n<td>Output needs manual clarification<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Unusable Source<\/td>\r\n<td>Source quality does not support routine processing<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Reconciled<\/td>\r\n<td>All incoming items accounted for<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n\r\n<p>\r\nThis is the same control principle explained in our\r\n<a href=\"\/blogs\/data-reconciliation-workload-control\/\">workload reconciliation article<\/a>.\r\n<\/p>\r\n\r\n<h2>13. OCR and Data Entry Often Work Together<\/h2>\r\n\r\n<p>\r\nOCR can accelerate the first stage of text capture, while data-entry review can support structured field mapping, validation and exception handling.\r\n<\/p>\r\n\r\n<p>\r\nA combined workflow may look like:\r\n<\/p>\r\n\r\n<div class=\"gdes-blog-highlight\">\r\n<strong>SCAN \u2192 OCR \u2192 CLASSIFY \u2192 MAP \u2192 VALIDATE \u2192 REVIEW EXCEPTIONS \u2192 RECONCILE \u2192 DELIVER<\/strong>\r\n<\/div>\r\n\r\n<p>\r\nFor broader capture requirements, our\r\n<a href=\"\/data-entry-services\/\">data entry services<\/a>\r\ncan support client-defined structured processing after document recognition.\r\n<\/p>\r\n\r\n<h2>14. Document Processing Adds Context Around OCR<\/h2>\r\n\r\n<p>\r\nOCR is often only one component of a larger document workflow.\r\n<\/p>\r\n\r\n<p>\r\nThe complete process may also include:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>File preparation<\/li>\r\n<li>Document classification<\/li>\r\n<li>Page separation<\/li>\r\n<li>Indexing<\/li>\r\n<li>Field extraction<\/li>\r\n<li>Validation<\/li>\r\n<li>Exception management<\/li>\r\n<li>Structured output preparation<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nOur\r\n<a href=\"\/document-processing\/\">document processing services<\/a>\r\nsupport these broader administrative document workflows.\r\n<\/p>\r\n\r\n<h2>15. OCR Output vs Validated Data<\/h2>\r\n\r\n<div class=\"gdes-blog-table-wrap\">\r\n<table class=\"gdes-blog-table\">\r\n<thead>\r\n<tr>\r\n<th>OCR Output<\/th>\r\n<th>Validated Data<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td>Recognized text<\/td>\r\n<td>Text reviewed against defined rules<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>May contain character errors<\/td>\r\n<td>Critical fields checked<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>May lose table structure<\/td>\r\n<td>Target field structure confirmed<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>May include uncertain values<\/td>\r\n<td>Uncertainty routed to review<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>May not explain failed documents<\/td>\r\n<td>Full source population reconciled<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n\r\n<h2>How Outsourcing Can Support OCR Cleanup and Validation<\/h2>\r\n\r\n<p>\r\nLarge scanning projects can create substantial post-recognition review work.\r\n<\/p>\r\n\r\n<p>\r\nA structured outsourcing model can support client-defined activities such as:\r\n<\/p>\r\n\r\n<ul class=\"gdes-blog-list\">\r\n<li>OCR output cleanup<\/li>\r\n<li>Document classification<\/li>\r\n<li>Field mapping<\/li>\r\n<li>Manual validation<\/li>\r\n<li>Exception identification<\/li>\r\n<li>Source referencing<\/li>\r\n<li>Structured data entry<\/li>\r\n<li>Reconciliation<\/li>\r\n<\/ul>\r\n\r\n<p>\r\nGlobal Data Entry Solutions provides\r\n<a href=\"\/scanning-and-ocr-services\/\">scanning and OCR services<\/a>\r\nalong with\r\n<a href=\"\/ocr-cleanup-processing\/\">OCR cleanup processing<\/a>\r\nfor administrative document-processing requirements.\r\n<\/p>\r\n\r\n<h2>Frequently Asked Questions<\/h2>\r\n\r\n<div class=\"gdes-blog-faq\">\r\n<h3>Is OCR output always accurate?<\/h3>\r\n<p>\r\nNo. OCR results can vary based on scan quality, document layout, fonts, image clarity and other source factors, so important fields may require review.\r\n<\/p>\r\n<\/div>\r\n\r\n<div class=\"gdes-blog-faq\">\r\n<h3>What is OCR cleanup?<\/h3>\r\n<p>\r\nOCR cleanup is the post-recognition process of reviewing and correcting recognized text, structure, formatting and client-defined fields before the output is used or delivered.\r\n<\/p>\r\n<\/div>\r\n\r\n<div class=\"gdes-blog-faq\">\r\n<h3>Why should OCR output be validated?<\/h3>\r\n<p>\r\nValidation helps confirm that recognized information matches the source and has been mapped to the correct field or target structure.\r\n<\/p>\r\n<\/div>\r\n\r\n<div class=\"gdes-blog-faq\">\r\n<h3>How should unreadable OCR fields be handled?<\/h3>\r\n<p>\r\nIf the source does not support a reliable value, the field or record should be flagged for review rather than guessed.\r\n<\/p>\r\n<\/div>\r\n\r\n<div class=\"gdes-blog-faq\">\r\n<h3>What is the difference between OCR and data entry?<\/h3>\r\n<p>\r\nOCR recognizes text from images or scanned documents, while data entry can involve structured field capture, mapping, validation and review according to client-defined requirements.\r\n<\/p>\r\n<\/div>\r\n\r\n<h2>Final Thought: Recognition Is the Beginning of the Data Workflow<\/h2>\r\n\r\n<p>\r\nOCR can make scanned information machine-readable, but business-ready data often requires additional structure and control.\r\n<\/p>\r\n\r\n<p>\r\nThe stronger workflow connects recognition with classification, mapping, validation, exception handling, traceability and reconciliation.\r\n<\/p>\r\n\r\n<div class=\"gdes-blog-highlight\">\r\n<strong>OCR tells you what the system recognized. Validation helps determine whether that recognized information is ready to use.<\/strong>\r\n<\/div>\r\n\r\n<div class=\"gdes-blog-cta\">\r\n<h2>Need Help With OCR Cleanup or Document Data Processing?<\/h2>\r\n<p>\r\nGlobal Data Entry Solutions supports scanning, OCR, cleanup, document processing and structured data-entry workflows using client-defined validation, exception and reconciliation rules.\r\n<\/p>\r\n<a class=\"gdes-blog-btn\" href=\"\/contact-us\/\">Discuss Your Requirement<\/a>\r\n<\/div>\r\n\r\n<\/article>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>OCR can make scanned text machine-readable, but recognized text is not automatically clean data. 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