Automating VIN Data Entry in Automotive Manufacturing with RS38 and Pic 'n Fill™ OCR

OCR-enabled VIN capture in automotive manufacturing

A globally renowned automobile manufacturer , with a significant presence in India since the early 2000s, has built an integrated operational ecosystem that includes a cutting-edge manufacturing plant, a strategically located parts warehouse, and a fully equipped training center. Employing more than 650 personnel, the manufacturer has also established a strong dealer network in metropolitan cities to support its premium automotive offerings. To ensure production traceability and process integrity, the company uses a Manufacturing Execution System (MES) on its shop floor.

A critical element of this system involves the capture and entry of engraved VIN plate data into the shop floor control interface. This data includes the vehicle identification number (VIN), engine number, part number, and production date codes. Traditionally, operators were required to visually read each VIN plate and manually key in this data—a process that was not only time-consuming but also susceptible to input errors.

Challenges in Manual VIN Data Entry

The manufacturer faced significant issues with its manual VIN data capture workflow. Data entry relied heavily on human accuracy, which made it vulnerable to mistakes such as misreading the VIN or incorrectly inputting alphanumeric characters. Each incorrect entry had serious consequences, often resulting in legal documentation corrections and the need to transport affected vehicles back to the factory—an expensive and inefficient resolution. Additionally, the manual process significantly hindered production speed and reduced the overall shop floor efficiency.

Implementing VIN Capture with CipherLab RS38 and OCR

 To address these challenges, CipherLab provided its RS38 touch mobile computer equipped with the Pic 'n Fill OCR solution. Working closely with the manufacturer's team, CipherLab collected photo samples of VIN plates and trained its OCR engine tailored specifically for the engraved VIN plate format. Since these plates could be difficult to read—especially under poor lighting or due to variations in vehicle paint color—accurate recognition was critical to minimizing errors.

In a remarkably short timeframe, CipherLab developed and deployed a customized version of the Pic 'n Fill application that could extract VIN data with high accuracy. The solution incorporated VIN plate data rules to enhance recognition reliability. To overcome issues with lighting and color contrast, particularly in light-colored vehicles, CipherLab also introduced preset camera adjustments such as lens exposure and contrast settings, accessible via a dedicated preset switch. This ensured consistent performance even in suboptimal visual conditions.

Instead of manually entering data, workers now use the RS38 handheld computer to capture the VIN plate data. The Pic 'n Fill application automatically processes the image and converts the extracted data into a QR code. This QR code is then displayed on the device screen and scanned by the existing shop floor workstation, the entire data entry process to be completed quickly and seamlessly.

Delivering Efficiency and Accuracy with VIN Automation

The implementation of the automated VIN data entry workflow has increased operational efficiency by over 90%, dramatically shortening the time required to capture and input VIN data. This transformation has not only accelerated the production process but also significantly improved data accuracy. Instances of human error have been virtually eliminated, along with the costly need to return vehicles to the factory due to incorrect VIN entries. The manufacturing operations manager expressed high satisfaction with the solution's precision, speed, and ease of use. As a result, the VIN recognition system will be rolled out across all relevant workstations. CipherLab's rapid development and agile site support played a crucial role in achieving a successful deployment, offering the manufacturer a cost-effective, scalable, and high-return investment in operational innovation.