Optical Character Recognition (OCR) has been around long enough that most people think of it as a data extraction problem that has been solved for good. However, it’s not. OCR is useful, and in many situations it saves hours of manual work, but it also has limitations. Many organizations learn this the hard way when they try to process large sets of documents, only to find that OCR alone can’t deliver the accuracy they expected to be fully relied upon.
What is OCR?
OCR is a technology that converts scanned paper files, PDFs, or camera images into editable and searchable text. It takes information that appears readable to the human eye but exists as unstructured data and turns it into structured data that computers can process. A scanned image may look like text, but to a computer, it’s nothing more than pixels. OCR gives systems the ability to read those pixels as letters, numbers, and symbols. Once that happens, the text becomes usable for indexing, editing, and data entry.
6 Challenges of OCR and Ways to Tackle Them
Helpful as that is, OCR still has challenges. Here are the 6 most common issues and the practical ways to work around them.
- Poor Image Quality
OCR accuracy drops sharply when the source image is blurry, low-resolution, or poorly lit. It often happens with old scanned documents, unclear photocopies, or handwritten notes. Even a slight shadow can distort characters enough to confuse the software.
The best way to overcome this challenge is rather simple: improve the quality of the original documents. Set a rule that every scan must be at least 300 dpi. If employees are taking photos, train them to use steady framing, good lighting, and angles that avoid glare. None of this is complicated, but it makes a noticeable difference. If the images are still tricky, you may have to move to a higher-quality OCR platform that can handle difficult inputs more reliably. Or simply, you can outsource it to a trusted OCR partner that can handle it all for you.
- Variable Document and Layout Formats
OCR works well when everything looks the same. The problems start when files arrive in different layouts, or when the visual formatting carries meaning. A table may use spacing to communicate relationships. A letter may rely on indentation or section breaks to guide interpretation. OCR often ignores these cues. It extracts the text but leaves the structure behind.
A related issue is that many OCR systems operate on strict, predefined rules. Anything outside that set pattern gets misread. Documents arranged in multiple columns or mixed with graphical elements can confuse the system.
One way around this is to introduce universal file converters into your process. These tools convert PDFs, TIFFs, JPEGs, and other formats into a consistent baseline before OCR touches them. You can also reduce the confusion around layouts by standardizing formats across teams. When everyone uses the same structure, you cut down the number of odd cases that trigger OCR errors.
- Language and Character Set Limitations
OCR handles standard fonts and the Latin alphabet fairly well. Once you introduce unusual fonts, cursive handwriting, non-Latin scripts, or languages with complex characters, the accuracy takes a hit. Organizations with international clients see this most often.
There isn’t a workaround for this other than picking the right platform or a professional OCR partner. Choose a system with strong multi-language support and good optimization for mixed content. That means it should be able to interpret numeric and alphabetic characters together instead of treating them as separate sets. The difference in performance can be dramatic, especially when handling languages such as Arabic or East Asian scripts. A professional partner like Rannsolve will have the in-house tools and resources needed for high-accuracy OCR outputs.
- Text Distortion and Skew
OCR assumes the text runs horizontally or vertically. Anything outside that range creates trouble. A slight tilt in the scan can cause whole lines to be misread. Curved pages, folded corners, and uneven camera angles add more distortion.
Pre-processing is a good choice to make this right. Apply skew correction and alignment tools before feeding images into the OCR system. Many modern platforms offer automatic correction, though it works best when the input isn’t badly warped. It also helps to reinforce better habits for physical handling. Encourage staff to flatten documents before scanning and to check alignment on the scanner bed or camera frame. A few seconds of preparation saves time on the back end.
- Complex or Non-Textual Elements
Tables, graphs, equations, symbols, and logos often confuse OCR systems because they look like text in some places and like images in others. When the system guesses wrong, it may interpret a symbol as a letter or treat a shape as a word. This is especially common in technical documents or forms with mixed elements.
A good approach is to filter out non-text elements before OCR begins. Pre-processing tools can remove or mask logos and graphical shapes so the engine focuses only on actual text. If your documents rely on complex mixes of imagery and text, consider a specialized OCR system designed to extract embedded text accurately.
Outsource Your OCR Operations to Rannsolve
If you’d rather avoid these challenges altogether, you can outsource your OCR operations to Rannsolve. With 25+ years of data services and having digitized over 10 billion, Rannsolve combines AI-driven tools to extract information from unstructured documents across all formats. Our in-house systems turn scattered, inconsistent data into clear and actionable results while reducing the manual effort on your end. Get in touch with our OCR expert now.
