Now is the time for engineers to leverage the power of deep learning and think like data scientists
The Indian automotive industry is on the brink of disruption due to the rise of technology with trends like shared mobility, autonomous driving, and connectivity. As more people turn to green mobility, the Indian electric vehicle (EV) market is booming, and India is experiencing a faster increase in EV adoption. According to Cyber Media Research’s (CMR) latest auto market report for Q1 2023, the market for connected vehicles led by EVs has witnessed a growth of around 60%. As a result, manufacturers are facing challenges in maintaining the demand and supply momentum of the hybrid and EV automotive industry. To address this, Indian manufacturers need to create a robust value chain including the production and supply of raw materials and batteries to increase the efficiency of the sector in the near future.
According to Zebra’s recent Automotive Ecosystem Vision Study, eight out of 10 industry decision-makers surveyed in the APAC region including India agree that their organizations are struggling to keep pace with the speed of technological innovation, with “developing software expertise” cited as a top five investment priority. A recent industry report agrees, saying recent developments in robotics, Artificial Intelligence (AI), and machine learning have put us on the cusp of a new automation age.
One key technology innovation rising in importance is deep learning optical character recognition (OCR) software. According to the same Zebra automotive study, more than one-third of APAC original equipment manufacturers (OEMs) surveyed believe that technologies including industrial machine vision will improve supply chain management.
The combination of deep learning and OCR meets the need for speed, accuracy, and reliable solutions for compliance, quality, and presence checks across manufacturing industries. Operations leaders within the automotive, pharmaceutical, electronics, and food and beverage industries are likely to gain the most with deep learning OCR.
Smart Manufacturing
Machine vision and deep learning OCR are enabling smart manufacturing, which Gartner defines as orchestrating physical and digital processes within factories and across other supply chain functions. These transform the ways people, processes, and technology operate to deliver the information needed to impact decision quality, efficiency, cost, and agility. In other words, cultivating automation via deep learning and OCR helps to bring out the best in hardware, software, and people.
But getting OCR inspection right can be challenging. Stylized fonts, blurred, distorted, or obscured characters, reflective surfaces and complex, non-uniform backgrounds can make it impossible to achieve stable results using traditional OCR techniques.
However, there are new tools on the market that offer industrial-quality deep learning OCR and come with ready-to-use neural networks pre-trained using thousands of different image samples. This newer range can deliver high levels of accuracy straight out of the box, even when dealing with very difficult cases.
In an automotive manufacturing setting, it means a deep learning OCR solution can accurately read printed, embossed, matte, and metallic serial numbers stamped on batteries, tires, parts, and accessories to ensure they correspond with the correct car model’s vehicle identification number (VIN). These solutions can also handle a range of font styles and sizes, ‘harsh’ lighting, and manufacturing environments.
Deep learning OCR can also be utilized as part of a wider machine vision solution. For example, in an automotive manufacturing setting, a machine vision solution can be deployed for presence/absence, quality and compliance for connector pin inspection, conformal coating on printed circuit boards, adhesive inspection, wire harnessing, electric battery and polarity, and general assembly verification.
In these scenarios, machine vision solutions deployed using the same machine vision cameras and unifying software platform can inspect items at a much faster speed, flagging suspected defects and errors to an engineer to examine and decide whether it is a fault or not and move on. Feeding the review decision back into the neural network helps to keep the continual learning input active – to further develop and enhance the model. It optimizes efficiency and removes an important yet tedious manual task from engineers.
The Growing Value of Deep Learning
Deep learning’s speed and accuracy enable it to assist engineers, ensuring quality in manufacturing, controlling production costs, and enhancing customer satisfaction. However, ease of use is equally valuable, and that is where deep learning OCR software shines. It is an easy application to implement and use and can be deployed in a few simple steps—all without the need for machine vision expertise.
Together, more accessible machine vision and deep learning OCR solutions are unlocking new possibilities for industrial imaging professionals and engineers to think and act more like data scientists. This development is needed and welcomed, in the face of ever-growing data velocity, volume, and variety, as well as the higher levels of speed, safety, and accuracy expected.
(This article is authored by Rajnish Gupta, VP and Head, India and Subcontinent business, Zebra Technologies APAC)
