Optical Character Recognition (OCR)

Short answer: To a computer, a scanned order form or a photo of a job sheet is only a picture. Optical character recognition, or OCR, identifies the letters, numbers and words in that picture and turns them into text. It is a common reading step in document automation. On its own, OCR does not know which number is the order total or which name is the customer. Intelligent document processing goes further: it sorts documents, pulls out specific fields and checks them. OCR can be one step inside it.

Common uses include making archives of scanned contracts searchable and turning paper forms into digital records. OCR is also the first step in automating data entry from order forms, job sheets and delivery notes, and in reading identity documents. Accuracy depends on image quality, including resolution, tilt, blur, shadows and faded print. It also depends on layout, fonts and language. OCR usually reads clean printed text well. Handwriting, especially joined-up writing, is usually harder to read accurately than print. PDFs that software creates directly usually already contain text, and a system can read that text more reliably without OCR.

The process starts with an image from a scanner, a phone camera or a document upload. The software cleans the image by straightening it, removing specks and adjusting contrast. It then analyzes the layout to find blocks of text, lines, columns and tables. Next, it recognizes each word or character. Dictionaries and language rules then correct likely errors. The output is plain text or a searchable PDF. A searchable PDF keeps the original image and adds a hidden layer of text behind it. The software often records the position of each word and a confidence score, which shows how sure it is. A reviewer can then check the uncertain results.

A field service company receives 1,200 scanned job sheets a month from its engineers. Office staff type the customer, date and job number into the job system by hand, at 4 minutes a sheet. That is 80 hours a month. The company adds OCR plus an extraction step that picks those three fields out of the recognized text. In a test of 200 job sheets, 170, or 85%, have every field correct. At monthly volume, that means 1,020 correct sheets and 180 with errors. Checking each correct sheet takes 1 minute, or 17 hours in total. Fixing each sheet with errors takes 3 minutes, or 9 hours. The new total is 26 hours, a saving of 54 hours a month.

Paper and scanned documents are a common source of manual data entry in growing companies. Leaders should treat OCR as the reading step. Most of the business value comes from what happens after it: pulling out the right fields, checking them and entering them into systems. Scoping a project starts with four questions. How many documents arrive as scans rather than digital files? How good are the images? How will the team measure accuracy for whole fields? Who reviews the uncertain results?

OCR errors can be small and hard to spot, such as a zero read as the letter O or a misplaced decimal point. In quantities, prices or customer numbers, one wrong digit matters. Accuracy figures for clean sample documents rarely match real scans, so test on the company's own documents. Accuracy per character also overstates accuracy per field, because one wrong character makes a whole field wrong. Scans may contain personal data, and online OCR services send images outside the company. Keep the original image linked to the extracted data, so anyone can check it later.