Optical Character Recognition (OCR)

Microscan Leads Optical Character Recognition Technology

Optical Character Recognition, commonly known as OCR, is simultaneously machine-readable and human-readable text. Common industries and applications include date/lot tracking on pharmaceutical or food packaging, sorting mail at post offices and other document handling applications, reading serial numbers in automotive or electronics applications, and many more.

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Optical Character Recognition technology has been used extensively in commercial applications since the 1970s, and is used today for automating tasks such as passport processing, secure document processing (checks, financial documents, bills), postal tracking, publishing, consumer goods packaging (batch codes, lot codes, expiration dates), and clinical applications. OCR readers and software can be used, as well as smart cameras and vision systems which have additional capabilities like barcode reading and product inspection.

The primary advantage of OCR is that it encodes information in a format that is both machine-readable and human-readable, while barcodes and 2D symbols are only machine-readable. However, data encoded in barcodes is considerably more reliable since OCR can have a high rate of character substitution (particularly with OCR-A and OCR-B fonts). Check characters are often embedded in OCR data fields and then calculated by OCR readers or vision systems to avoid substitution errors in data output.

There are different ways to integrate OCR into an application, and different systems for processing OCR-encoded data. OCR templates and OCR fonts are the simplest and most reliable option. Some examples of common OCR fonts include OCR-A, OCR-B, MICR E-13B, and SEMI M12. OCR templates define several parameters, including the OCR font, layout of OCR text (in a row, in a column, etc.), the number of characters in a row, the total number of rows, etc. 

OCR Examples
Examples of OCR on labels and directly marked

OCR Font Examples
Examples of some OCR fonts

Typically a feature of higher-end machine vision, teachable OCR systems can be trained to recognize characters in any user-defined font, not just specialized OCR fonts (OCR-A, OCR-B, MICR, SEMI), and can be taught to recognize a full character set in any font created for any language. The disadvantages of this type of system are the labor-intensive integration process and the decrease in reliability when using non-OCR fonts. Optical Character Verification (OCV) is one way to address the problem of reliability. Once the desired specifications have been taught to an OCR reader, OCV software can verify that the printed characters  match the specifications, can ensure that data is encoded correctly, and can guarantee that labels are placed in the correct orientations on the correct items.