OCR (optical character recognition) is software that turns images of text, such as scanned pages, photographed delivery notes or exported PDFs, into machine-readable text that other programs can search, count and edit.
No jargon, no math
You feed it a picture of a page and it hands you back the words as text. Inside, four things happen in order. First the image is cleaned: skew, smudges and noise are corrected until the lines sit straight. Then it is segmented into text blocks, lines, words and single characters. Each character is recognised by matching its shape against thousands of learned glyphs, and the recogniser returns a confidence score for every guess. Finally you get a text file plus a list of the doubtful characters. That is the boundary: handwriting, blurry faxes and ornate fonts still trip it up, so a human checks the flagged spots before anything enters your accounts.
Scan. The page arrives as an image, pixels with no meaning yet.
Clean. Skew, noise and smudges are corrected until the lines sit straight.
Segment. Text blocks become lines, lines become single characters.
Recognise. Each character is matched against learned shapes and gets a confidence score.
Check and output. High-confidence characters become searchable text; the doubtful ones are flagged for a human.
Unstructured data is information with no fixed schema: documents, emails, images, audio, chat logs, PDFs. Most of what a company knows lives in this form, but software must first parse, transcribe or embed it before it can be searched or counted.
ReadA data pipeline is the connected chain of steps that carries data from where it is produced to where it is consumed: ingest, validate, transform, store and serve, each stage handing clean, well-formed data to the next.
ReadETL (extract, transform, load) is the process of pulling data out of source systems, reshaping it to fit a target schema, and loading it into a warehouse or database where reports and AI can rely on it.
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