OCR Explained

Overview

OCR (optical character recognition) analyzes an image of text—usually a scan or photo of a page—and estimates the characters that appear there. The result can be a plain-text extract, a searchable text layer under the page image, or an editable approximation of the original layout.

OCR quality depends on capture quality: resolution, contrast, skew, language models, and whether the “text” is actually stylized lettering. Clean 300 dpi grayscale scans of printed documents typically outperform phone photos of glossy contracts under mixed lighting.

Dockzio does not currently ship a dedicated OCR tool. You can still improve OCR outcomes elsewhere by preparing pages well: straighten and crop scans, compress responsibly, and convert PDF pages to images when a vision or OCR pipeline expects image input. Tools such as pdf-to-jpg, pdf-crop, and pdf-compress are useful in that prep path.

Step-by-step

1. 1. Decide what “searchable” means for your job

   Sometimes you only need to find keywords; a hidden text layer is enough. Other times you need copy-paste fidelity or true editing—expectations that demand higher OCR accuracy and often human review.

   For legal or archival work, treat OCR output as an aid, not an authoritative transcript, unless your process includes verification.

2. 2. Capture or export pages at a workable resolution

   Aim for roughly 300 dpi at the final reading size for Latin text. Very low resolution forces the OCR engine to guess; extremely high resolution can slow processing without proportional gains.

   If your source is already a PDF of scans, use pdf-to-jpg to produce page images for an external OCR service, or crop away dark borders with pdf-crop so the engine focuses on text regions.

3. 3. Improve contrast and geometry before OCR

   Deskew crooked pages, remove heavy shadows, and avoid compressing text scans with aggressive JPEG settings that smear character edges.

   When a PDF mixes vector text pages and scanned pages, split them first. Running OCR on pages that already have real text can create duplicate, conflicting text layers.

4. 4. Run OCR in a tool that matches your language and layout

   Choose an OCR engine that supports your languages and any specialized vocabularies (legal, medical, engineering). Multi-column layouts and tables need engines that preserve reading order.

   After OCR, spot-check proper nouns, numbers, and punctuation—the tokens that matter most when errors are costly.

5. 5. Store both the image truth and the text layer thoughtfully

   Keep the visual page as the human-readable source of truth when the scan is evidence. The OCR layer is an index and convenience copy.

   If you must shrink the package for sharing, compress after OCR and confirm the text layer still selects correctly over the image.

Common mistakes

• Expecting perfect OCR from phone photos. Perspective distortion, blur, and glare produce plausible but wrong words. Reshoot flat under even light when accuracy matters.
• Heavy compression before OCR. Blocky JPEG artifacts around glyphs confuse character segmentation. Compress for delivery after recognition, not before.
• Assuming every PDF needs OCR. Born-digital PDFs already contain real text. OCR is for image-based pages; applying it blindly can create messy double text.