7 Ways to Improve OCR Accuracy in gImageReader (DPI, Preprocessing & More)

BygImageReader Team
⚡ Quick Answer

The fastest accuracy wins: scan at 300 DPI, select the correct language, use the tessdata_best models, and preprocess poor images (deskew, increase contrast, binarize) in gImageReader’s Image Controls before recognizing. These four cover 90% of bad OCR output.

Getting gibberish from Tesseract? The engine is rarely the problem — the input is. These 7 techniques, ordered by impact, turn messy OCR output into clean text. All of them work directly in gImageReader.

1

Scan at 300 DPI (or higher)

The single biggest factor. Tesseract wants characters roughly 30–60 pixels tall. A 150-DPI scan of small print starves the engine; 300 DPI is the sweet spot, 600 DPI for tiny fonts. Rescanning beats every software trick below.

2

Select the correct language

Recognizing German text with the English model guarantees errors. Set the toolbar language to match your document — and install the language pack first if it’s missing. For mixed-language pages, select multiple languages at once.

3

Use the “Best” traineddata models

Tesseract ships three model tiers: fast (small, quick), standard, and best (large LSTM, most accurate). For important documents, download the tessdata_best files — slower, but visibly more accurate on difficult scans.

4

Deskew crooked scans

Even a 2–3° tilt confuses line detection. Straighten pages before recognizing — gImageReader’s Image Controls include rotation; for badly skewed batches, a quick pass in any image editor pays off.

5

Binarize washed-out images

Gray, low-contrast scans (old faxes, phone photos) should be converted to pure black-and-white before OCR. Use Image Controls → Binarize in gImageReader and nudge the threshold until text is crisp and backgrounds disappear.

6

Increase contrast on phone photos

Uneven lighting is the phone-photo killer. Boost contrast and brightness so text stands out from shadows, then binarize. If a photo is curved or shadowed beyond repair, rescanning beats processing.

7

Define zones manually for complex layouts

Auto-detection can misread multi-column pages, reading across columns. Drawing manual recognition zones forces the correct reading order — the fix for most “words in wrong order” complaints. Our tutorial shows how.

Know the limits: No preprocessing fixes handwriting or complex table grids — Tesseract reads lines, not layouts. For those, see when it’s worth upgrading in our ABBYY comparison.

Frequently Asked Questions

Why is my Tesseract OCR output full of errors?
In most cases it’s the input, not the engine: DPI too low, wrong language selected, skewed or low-contrast images. Work through the 7 techniques above in order — scanning at 300 DPI and selecting the correct language fix the majority of cases.
Does higher DPI always mean better OCR accuracy?
Up to a point. 300 DPI is the sweet spot for normal print; 600 DPI helps tiny fonts. Beyond that, accuracy plateaus while files get huge and processing slows down. For phone photos, good lighting matters more than raw resolution.
What are tessdata_best models and should I use them?
tessdata_best contains Tesseract’s most accurate LSTM models — larger files and slower recognition, but noticeably better results on difficult scans. Use them for important documents; the smaller “fast” models are fine for quick drafts.

Put these techniques to work

Download gImageReader free — Image Controls and Best models included.

Download gImageReader Free

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