AI OCR in eViewer: Turn Scanned Documents into Searchable & Usable Text
OCR turns scans, faxes, and images into digital text. eViewer’s AI OCR goes further. It reads the page layout, including columns and tables, handles scanned PDFs, TIFFs, and handwritten documents, and feeds the results into PII detection, search, comparison, and your ECM.
What is AI OCR?
Traditional OCR compares each character on a page against a fixed set of templates. It works well on clean scans and falls apart when a page is skewed, faded, or came in through a fax line.
AI OCR is a label, not a single technology. It usually bundles three separate jobs: recognizing characters, understanding how they are arranged on the page, and interpreting what they mean. eViewer covers all three in one viewer. eViewer’s OCR is built using docTR, an open-source deep-learning text recognition library written in Python.
| Layer | What It Does | In eViewer |
| Character Recognition | A deep learning model finds the text on the page and reads each word. | eViewer uses docTR for text detection and recognition on scanned PDFs, TIFFs, and other image documents. |
| Layout Analysis | Works out where columns start and end, which text belongs in a table, and which blocks are paragraphs. | eViewer identifies columns, tables, graphs, and paragraphs, so a page does not collapse into one flat run of text. |
| Meaning | Gives the recognized text context, such as which values are fields worth capturing. | eViewer captures fields such as names, dates, and numbers, detects PII, and supports AI-powered search by meaning. |
docTR: Detection, then Recognition
docTR is an open-source OCR library built on deep learning. It works in two stages. First, it detects where text sits on the page. Then it recognizes the characters in each word. eViewer uses docTR in its AI OCR, so a scanned PDF, a TIFF, a fax, or another handwritten document gets the same two-stage treatment.
Layout-Aware OCR
Basic OCR outputs text as one continuous stream, with no sense of where anything sat on the page. That is fine for a simple letter. On an invoice, a two-column report, or a form full of tables, it mixes up the reading order and separates values from their labels. eViewer’s layout-aware OCR keeps the page structure, so the text comes out in the same order and arrangement a user would read it.
Where OCR Fits in Your Document Workflow
OCR generates the text that optimizes document workflows. Better text at the start means fewer errors at every stage after it.
| Stage | Name | What happens in eViewer |
| 1 | Prepare | Scanned and faxed pages arrive in different layouts and styles. eViewer opens PDFs and TIFFs in a single viewer, so every file is handled the same way. |
| 2 | Generate | OCR turns the page image into text, following the layout, columns, and tables. |
| 3 | Extract | AI data extraction picks out the values that matter, such as PII, columns and table data, and any other required data. |
| 4 | Check | Extracted values are reviewed against the rules your team sets to avoid mistakes. |
| 5 | Finalize | The text is ready for search, redaction, comparison, text-to-speech, and the document can be saved back to your connected ECM repository, BPM, CRM, or other systems. |
Because reading, extraction, and review happen in one viewer, you are not using a variety of tools and multiple licenses that could cause incorrect text detection.
Documents eViewer Can Read
The same viewer handles the formats most teams deal with, from clean PDFs to old scanned archives.
| Document Type | How eViewer Handles It |
| Scanned PDFs | Image-based PDFs are made text-searchable, so you can search, highlight, annotate, and redact them like any other PDF. |
| PDFs and TIFFs | Both open in the same viewer. OCR runs when an operation needs the text; faxes saved as TIFFs can be searched, redacted, and compared side by side with text-based documents. |
| Handwritten Documents | Handwritten text is supported, alongside OCR for printed text, so filled-in forms and notes can be captured too. |
What You Can Do with the Recognized Text
Once the OCR’d text is generated, users are able to use eViewer to extract data, search by meaning, detect PII, compare documents, and listen to the content.
Language and Framework Support
eViewer’s OCR is written in Python and exposed through Flask APIs, so it fits into the services your team already runs. Here is the stack behind it.
| Layer | Technology |
| Language | Python |
| Framework | Flask APIs, Redis, Celery |
| OCR engine | docTR |
| Embedding’s and transformers | FastEmbed Transformers |
Common Use Cases
eViewer’s OCR fits anywhere paper and scanned documents pile up. These are the most common deployment patterns.
| Industry / Use Case | How eViewer OCR Is Applied |
| Banking and Financial Services | Map data on forms and IDs to database fields to support account opening, loan applications, and customer onboarding. |
| Legal | Search, review, and redact scanned records for eDiscovery and privilege review. Compare contract versions, including scanned ones, to find what changed. |
| Healthcare | Digitize patient-related paper documents. Detect PII before documents move between systems, in support of HIPAA and other privacy requirements. |
| Government | Apply OCR to incoming form submissions so the documents become searchable and extractable. |
Frequently Asked Questions
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From Paper to Searchable Text.
See eViewer AI OCR on your own documents. Book a demo or talk to the team.