7 practical ways to get better OCR accuracy

OCR has become remarkably good, but it is not magic, and the quality of what you get out is tightly linked to the quality of what you put in. The encouraging part is that most accuracy problems come from a handful of avoidable issues at capture time. Fix those and you will often go from frustrating results to near-perfect ones without changing your tool at all. Here are seven things that reliably move the needle.

First, resolution matters more than almost anything else. Aim to scan documents at 300 DPI or higher. If you are photographing rather than scanning, fill the frame with the document so the text takes up most of the pixels instead of sitting tiny in the middle of a large photo. Small, low-resolution text is where engines guess most, and guessing is where errors come from. A little extra resolution buys a lot of accuracy.

Second, get the lighting right. Even, diffuse light is the goal. Harsh direct light creates glare and blown-out patches, while dim light forces the sensor to introduce noise that muddies letter shapes. When photographing a page, avoid casting your own shadow across it, and turn the page slightly to dodge reflections from overhead lights. This one change alone rescues a lot of receipt and document photos.

Third, keep pages flat and straight. A curled book page or a receipt that bows away from the camera distorts characters and confuses layout detection. Straighten what you can physically, and let the software deskew the rest. Many tools, FlowOCR included, correct moderate rotation automatically, but you make their job far easier by starting close to square rather than at a jaunty angle.

Fourth, lean on preprocessing rather than fighting it. Increasing contrast helps faint text separate from the background, converting to grayscale can reduce distracting colour noise, and cropping tightly to the content area stops the engine wasting effort on borders and backgrounds. Think of preprocessing as clearing the desk before you start reading - it does not change the words, it just makes them easier to see.

Fifth, match the file format to the situation. For photos of text, sharp lossless formats like PNG preserve edges better than heavily compressed JPEGs, where compression artefacts smear the fine detail that character recognition depends on. If you are working from PDFs, remember that a scanned PDF is really an image and needs OCR, while a digital PDF may already contain selectable text you can use directly.

Sixth, and this is the one people overlook, choose the right engine for the job. General-purpose text OCR is excellent for plain paragraphs but not built to preserve a table's structure. If your document is a table, use a table or image-to-Excel extractor that keeps rows and columns intact. If it is an invoice or receipt, a specialised extractor will pull named fields far more reliably than a generic reader. Using a tool designed for your document type is often a bigger win than any amount of image tweaking.

Seventh, always keep a human in the loop for anything that matters. OCR should speed up review, not replace it. Spot-check the output, and pay particular attention to the characters engines most commonly confuse - a zero versus a capital O, a one versus a lowercase L, an eight versus a B. Good tools let you edit the result in the browser before you export, so correcting the occasional slip takes seconds rather than a full redo.

It also helps to set realistic expectations by document type. Clean printed text is the easy case and will look almost flawless. Dense text with small fonts is a little harder. Handwriting is harder still and varies enormously with penmanship. Faded thermal receipts are among the toughest inputs there are, because the source itself is degrading. Knowing where your document sits on that spectrum tells you how much review to budget for.

If you process documents in volume, consistency is your friend. Standardise how documents are captured - the same scanner settings, the same photo setup, the same lighting - so every input arrives in roughly the same condition. When inputs are predictable, output quality is predictable too, and you can tune your process once instead of firefighting every batch. Automation thrives on that kind of sameness.

None of these tips is complicated, and that is rather the point. You do not need a better algorithm nearly as often as you need a better photo. Start at the top of this list - resolution, lighting, flatness - and you will resolve the majority of accuracy complaints before you ever touch a setting. Combine good capture habits with an engine suited to your document type and a quick human review, and OCR stops being something you fight and becomes something you rely on.