OCR limitations: 6 disadvantages of OCR and how to fix each one
For teams whose OCR output still needs fixing by hand: where optical character recognition breaks, what the errors look like on a degraded scan, and what closes each gap.
Key takeaways
- OCR limitations fall into six groups: poor image quality, tables and complex layouts, handwriting, unfamiliar fonts and languages, no idea what a value means, and no check that a value was read correctly.
- Image quality comes first: Tesseract's documentation says it works best on images of at least 300 DPI, and that a skewed page badly hurts how well it finds lines of text.
- OCR returns text, not data. It doesn't know which number is the invoice total, and it doesn't check that the total adds up.
- Intelligent document processing (IDP) runs after OCR: it rebuilds tables, labels fields, checks values and sends the fields it's unsure about to a person.
- OCR alone is enough for searchable text. Once values have to reach another system, you need the extraction and checks that come after it.
On this page
OCR limitations are the things optical character recognition can't do on its own. OCR misreads poor scans, loses the structure of tables and multi-column pages, struggles with handwriting and unfamiliar fonts or languages, and returns plain text with no idea what each value means or whether it was read correctly. Each limit has a fix, from better scans to the extraction, checks and review that intelligent document processing (IDP) adds after OCR.
This guide shows the six limits with the fix for each, what the errors look like on a degraded scan, and when OCR alone is enough.
How the OCR process works, and where it stops#
OCR turns a picture of text into characters: it cleans up the image, finds the lines and words, and matches each shape to a character. Modern engines read printed text in many languages, return each word's position on the page with a confidence score, and make scanned PDFs searchable. On clean printed pages they're fast and accurate. What OCR hands back, though, is text, not data.
- Scanned PDF
- Phone photo
- Fax
- 01Clean up the image
- 02Find lines and words
- 03Recognize characters
What is OCR walks through each step of the process.
The 6 limitations of OCR technology#
Poor image quality
Low resolution, skew, faint ink, shadows, stamps and fax speckle turn an 8 into a B, hide a date under a stamp and split one line into two. The fix: scan at 300 DPI or more, and straighten and clean the image before OCR.Tables and complex layouts
Columns shift, rows merge, and a table that runs onto the next page comes back as loose lines. Tesseract's own documentation says tables need custom layout analysis. The fix: extraction that rebuilds tables, including ones that run across pages.Handwriting
Engines trained on print misread or skip handwritten entries, and some read handwriting in one language only: Amazon Textract's handwriting support is English only. The fix: a model trained on handwriting, with unsure fields sent to a person.Fonts and languages
An engine reads only the scripts and fonts it was trained on, so stylized fonts, look-alike characters such as 0 and O, and mixed-language pages cause misreads. The fix: an engine that covers your languages, tested on your real documents.No field meaning
OCR returns "656.40" but not whether it's the total, the tax or one line's amount. The fix: extraction that labels each value from its layout and context.No validation
OCR scores how sure it is of each word but doesn't check whether a value makes sense, so a confident misread flows into your systems unnoticed. The fix: checks such as line amounts that add up to the total, and review of low-confidence fields.
Real documents fail in the same places. In our OCR benchmark on 120 documents, the systems tested differed most on old scans, stamps, vertical text and small tables.
How to get better results from OCR#
Before you change tools, fix the input. Tesseract's documentation notes that, unless you use a very unusual font or a new language, retraining the engine is unlikely to help, and it lists input fixes instead. Most apply to any OCR engine:
- Scan at 300 DPI or moreRescan or re-export low-resolution images rather than fixing misreads afterward.
- Straighten the pageDeskew so text lines run level; a tilted page breaks line finding.
- Clean the backgroundRemove speckle, shadows and dark scanner borders, which can be read as extra characters.
- Tell the engine what to expectSet the languages and the page layout, and limit fields such as amounts to digits.
- Handle tables separatelyUse layout analysis that finds the cells, or an extraction tool that rebuilds tables.
- Measure on your own documentsScore a sample field by field before and after each change.
OCR alone vs OCR plus IDP#
OCR alone is enough when you need searchable text: archiving, making scanned PDFs searchable or copying a paragraph. Once the values have to reach another system, what happens after OCR decides how much manual work is left.
OCR alone
- One block of text per page, not named fields
- Tables flattened into loose lines
- Misreads pass through unnoticed
- Someone keys and checks each value by hand
OCR plus IDP
- The invoice number, date and total, each in its own field
- Tables rebuilt, even across pages
- Unsure fields flagged for a person to check
- Checked values sent to your systems by API
IDP vs OCR covers the difference in more depth.
The bottom line#
OCR reliably turns clean printed pages into text. Its limits show up on poor scans, tables, handwriting and unfamiliar fonts, and in everything after the text: knowing what a value means and whether it's right. Fix the image first. Then, for documents where errors cost you, add extraction, checks and review. Docsumo does that for invoices, bank statements, ACORD forms and any other document type, with 99% field-level accuracy on 250+ document types. It reads printed and handwritten text, joins tables that run across pages and sends the fields it's unsure about to your reviewers.
Book a demo with a few of your hardest documents, or start a free trial.
Frequently asked questions#
What are the limitations and disadvantages of OCR?
OCR misreads poor-quality images, loses the structure of tables and multi-column pages, struggles with handwriting and unfamiliar fonts or languages, and returns plain text without knowing what a value means or whether it was read correctly. Intelligent document processing adds the steps that close those gaps.
Why isn't OCR 100% accurate?
OCR matches shapes to characters, so anything that distorts the shapes causes errors: low resolution, skew, stamps, faint ink, decorative fonts and handwriting. Measure accuracy on your own documents, by field as well as by character; OCR accuracy explains how.
What can OCR do?
OCR turns printed text in scans, photos and image-only PDFs into machine-readable text, makes scanned PDFs searchable, and returns each word's position with a confidence score. Modern engines read many languages and some handwriting. It doesn't label fields or check values; that's what document processing adds.
Can OCR read handwriting?
OCR trained on print reads handwriting poorly. Modern engines and document AI platforms read handwriting, but less reliably than print, and some only in English. Send low-confidence handwritten fields to a person; see handwriting recognition.
What is the difference between OCR and IDP?
OCR turns images of text into characters. Intelligent document processing uses OCR as its first step, then classifies the document, extracts named fields, checks them and sends unsure fields to a person before the data reaches your systems. See IDP vs OCR.
How do you add OCR to a document processing pipeline?
Call a cloud OCR API or run an open-source engine to get text and word positions, then add the steps OCR doesn't do: table and field extraction, validation and a review screen. The best OCR APIs compares the options; a document processing platform includes those steps.
Sources
- Tesseract documentation: Improving the quality of the output
- AWS: Amazon Textract FAQs (language support)
First published . Last updated .