Optical Character Reading (OCR) and 1D/2D Code Verification

Parts and products are marked with expiration dates or product numbers. However, this is not useful when this information cannot be read, recognized, judged, stored, and managed accurately. Character inspection has become widespread in recent years for recognition or judgment based on characters extracted from captured image data. This page describes basic information about OCR (Optical Character Recognition) and OCV (Optical Character Verification) and the basic principle of character inspection.

Character strings are indispensable for showing information such as expiration dates for food/medicine and part numbers of assembly parts in manufacturing. They are also essential for sales order management. These strings are vitally important for management; however, management of characters can be a lot of trouble.

For example, assume that you manage orders and inventory on paper in handwriting. Management through handwriting may cause mistakes or omissions. If the book is lost, there might be no way to retrieve the orders and inventory.

In addition to preventing human errors, image processing is increasingly used to make management more reliable and reduce labor and costs. Replacing the recognition, checking and management of characters performed by human hands with an image processing system brings the following advantages:

Preventing mistakes and omissions in books
In character inspection, the captured characters are compared with those in the registered dictionary (character font groups), and each is recognized as the character with the closest shape. The recognized characters are compared with the judgment string, and the pass/fail output of the final inspection is produced. It is also possible to store the output judgment result on a server or other storage device. This type of reliable procedure prevents mistakes and omissions that may occur with management through handwriting.

Reliable information management
Character inspection using image processing technology allows operators to upload the recognized character information to a server or other device, so there are no worries about mistakes, such as writing the wrong characters. Product numbers or other items that consist of many characters are often written incorrectly. Handling them as digital information can prevent problems.

Thorough management of product numbers and other information also allows tracking of parts or products and collecting them quickly in case of the outflow of defective products or a product recall. It is also possible to track problems upstream to identify a problematic process to improve operations. Reliable information management is also effective for ensuring traceability.

Simultaneous reading along with barcodes or 2D codes
In recent years, barcodes or 2D codes have been introduced for product management. To read barcodes or 2D codes only, you can use a barcode reader or a handheld mobile computer. In many cases, however, an expiration date is also marked on the label along with the barcode or 2D code. Character inspection can recognize both codes and characters simultaneously. This improves efficiency dramatically because you can read all data at once and manage it together instead of reading each type of data separately.

100% inspection with minimum labor
The most effective method for preventing the outflow of defective workpieces is 100% inspection. However, checking characters visually not only takes time and labor but also involves a risk of oversight. Using image processing enables reliable inline character inspection to achieve both quality assurance and reduced labor costs. It also improves the yield rate in the inspection process and is highly effective for increasing production efficiency.

There are various types of character inspection: presence of marking, marking quality, optical character verification (OCV) and optical character recognition (OCR).

Presence of marking:

An inspection to detect the presence or absence of marking.

Marking quality:

An inspection to detect illegible characters.

OCV:

An inspection to compare marked characters and a judgment string, and make a pass/fail judgment.

OCR:

An inspection to read marked characters and output them for verification or control.

Although the purposes are different, the basic operation of character inspection is to extract characters one by one from a captured image, compare them with characters in the registered dictionary, and identify characters.

1. Character extraction

Extract characters one by one from the captured data.

2. Comparison against dictionary

Each of the extracted characters is compared with registered characters for identification.

Fixed cameras with tuned optics read linear codes such as UPC, EAN, Code 39, Code 128, Interleaved 2-of-5, Codabar, and Pharmacode at line speed. A 1D barcode reader tolerates skew, stretch, and moderate damage, while grading tools flag low-quality labels before they advance. Camera-based reading also adds unattended coverage where handhelds fall short.

2D tools decode compact symbols with high data density, such as Data Matrix (including ECC 200), QR and Micro QR, PDF417, and MaxiCode. Direct part marks on metal or molded parts are common in automotive and electronics. Vision-based decoders handle low contrast, curved surfaces, and glare through robust image formation and grading.

With machine vision OCR, lot numbers, batch codes, and expiration dates can be read even when fonts, print quality, or surfaces vary. OCR inspection uses trained character sets along with AI-based tools to pick up faint ink, reflective packaging, or etched markings. Results feed directly into recipes, work orders, or PLC tags, creating reliable data records that support product traceability.

Text and codes rarely travel alone. Combined tools read a 1D or 2D code and perform OCR inspection in the same acquisition. A single camera captures the label, verifies the barcode payload, and then matches it to lot and date fields. One station, one trigger, complete data capture.

Manual key-entry, visual checks, and spot audits create failure modes under pace and fatigue. Automated reading replaces subjective interpretation with deterministic tools. When the system finds a mismatch, it raises an output, stops a conveyor zone, or diverts a carton. Error paths shrink and repeatability rises.

Character strings and code payloads post to MES, ERP, or historians with timestamps and image archives. That stream supports genealogy, WIP reporting, and targeted recalls. With combined reading in place, product traceability moves from paper to structured data without adding operators or slowing the cell.

Camera-based readers run continuously at production speed, including on small fonts and narrow bars. Inline verification moves checks from end-of-line audits to the moment data is created. That shift shortens feedback loops for printers and lasers, and it reduces rework because failures are caught at the source.

By pairing machine vision OCR with code reading in the same job, facilities capture all identifiers in one frame. The system writes a single, consistent record containing the decoded barcode, OCR result, match status, grade, and image link. That record is easy to consume in dashboards and easy to trace when a quality event occurs.

Readers can grade symbols against common standards and detect low contrast, module damage, quiet-zone violations, or print shift. For challenging backgrounds (clear film, glossy cartons, or etched marks), specialized lighting and filters heighten contrast so the tool can decide pass/fail with a wide process window.

Registering several patterns for one character improves recognition stability. By registering various fonts as sub-patterns, you can prevent failed recognition due to different font types.

Automatic fitting is an automatic fine adjustment of characters to allow extraction in optimum conditions. Since slight variations in the status among characters are corrected individually before verification, the correlation level is increased.

Some further applications are:

• Food & Beverage: Read expiration dates with UPC or Data Matrix codes to prevent mixed lots.
• Pharmaceuticals: Tie lot, serial, and GS1 DataMatrix to item-level records, with OCR validating human-readable text.
• Automotive: Capture VINs and part numbers on castings and machined parts, even in oily or rough environments.
• Electronics: Read 2D codes and etched serials on boards and components for service traceability.
• Warehousing & Logistics: Log carton IDs while OCR checks printed labels for routing.