OCR Glossary: 25 Terms Every Beginner Should Know
OCR has its own vocabulary, and the jargon can make a simple task sound complicated. This glossary defines the terms you are most likely to meet, in plain language and in roughly the order they come up in a typical workflow.
OCR (Optical Character Recognition)
The technology that turns pictures of text into machine-readable characters you can copy, search, and edit. For the bigger picture, see What Is OCR?.
Tesseract
A widely used open-source OCR engine. It powers many free tools, including this one, and supports a dozen-plus languages out of the box.
Source image
The picture being read, whether a photo, a screenshot, or a scan. Its quality sets the ceiling on accuracy more than any other factor.
DPI (dots per inch)
A measure of scan resolution. For OCR, roughly 300 DPI on printed text is the sweet spot. Too low and characters blur together; far too high mostly wastes space.
Resolution
The pixel dimensions of an image. Higher resolution gives the engine more detail to work with, up to a point. See Best Image Resolution for OCR.
Preprocessing
Any cleanup applied before recognition: cropping, straightening, boosting contrast, removing noise. Good preprocessing often does more for accuracy than tweaking the engine.
Binarization
Converting an image to pure black and white so text stands out sharply from the background. A common preprocessing step.
Thresholding
The rule that decides which pixels become black and which become white during binarization. Adaptive thresholding adjusts locally, which helps with uneven lighting.
Noise
Random speckles, grain, or compression artifacts that the engine can mistake for characters. Reducing noise is a routine cleanup step.
Skew
The tilt in a scanned page when it was not perfectly straight. Even a few degrees can confuse line detection.
Deskew / skew correction
Rotating the image so text lines are level, which helps the engine group characters into words and lines correctly.
Segmentation
Breaking the page into regions, lines, words, and individual characters before recognition. Errors here cascade into the final text.
Layout analysis
Detecting the structure of a page, such as columns, paragraphs, and tables, so reading order comes out sensibly.
Bounding box
The rectangle the engine draws around a detected word or character. Useful when you want to know where on the page each piece of text sits.
Confidence score
A number the engine assigns to each recognized word or character, estimating how sure it is. Low-confidence words are worth proofreading first.
Character set / language model
The alphabet and word patterns the engine expects. Choosing the right language improves results dramatically, especially for accented or non-Latin scripts. See Multilingual OCR.
Ground truth
The verified, correct text used to measure how well an engine performed. Accuracy is the gap between OCR output and ground truth.
Character error rate (CER)
The percentage of characters the engine got wrong. A close cousin, word error rate (WER), measures wrong whole words.
Searchable PDF
A PDF that keeps the scanned image on top but adds an invisible text layer underneath, so you can search and copy. See What Is a Searchable PDF.
Text layer
The hidden, selectable text embedded behind a scanned image in a searchable PDF.
Handwriting recognition (ICR)
A specialized form of OCR aimed at handwritten input. It remains harder and less reliable than reading printed text.
Font hinting
How a typeface's design affects readability. Clean, common fonts read best; decorative, condensed, or stylized fonts trip the engine up.
Batch processing
Running OCR across many files in one go rather than one at a time.
Output format
How the result is delivered: plain text (.txt), Word (.docx), Markdown, and so on. The right choice depends on whether you need formatting.
Post-processing
Cleanup after recognition: spell-checking, fixing predictable errors (like 0 versus O), and reformatting. A quick human proofread is still the most reliable step.
Common questions
Do I need to learn all of these to use OCR?
No. Most tools handle preprocessing and segmentation automatically. Understanding DPI, resolution, and confidence helps you get better results, but you can start without any of it.
Which term matters most for accuracy?
Image quality, captured by resolution and DPI. A clean, well-lit, straight image of printed text beats any amount of after-the-fact tweaking.
Put it into practice
Now that the vocabulary makes sense, try it on a real file with Image to Text. It is free, requires no sign-up, and deletes your files automatically after processing.