Category: general

The power of visual intelligence (VI) is transforming industries. By leveraging the capabilities of computer vision and deep learning, AI is making machines “see” and understand the visual world like never before. The relentless march of automation is transforming factory floors and industrial environments. While robots have long been a fixture in manufacturing, a new wave of intelligent machines powered by visual intelligence (VI) is poised to revolutionize production.

This capability is leading to the development of AI-powered tools that are streamlining operations, enhancing safety, and improving quality control across diverse industries. From real-time anomaly detection to automated visual inspections, VI is empowering manufacturers to optimize production processes, minimize errors, and ensure worker safety.

Here are 5 of the most compelling Visual Intelligence AI products leading the charge in 2024

1. Scanflow: Real-time AI Safety Guardian for Industrial Workplaces

Keeping workers safe and products defect-free is paramount in any industrial setting. Scanflow empowers businesses with a robust AI system that tackles both safety and quality control. Here’s how it works: Scanflow integrates seamlessly with existing security cameras, eliminating the need for additional hardware installation. Its real-time AI core analyzes video feeds, proactively detecting unsafe situations like unauthorized access to hazardous areas or improper use of equipment. Scanflow goes beyond safety by continuously monitoring production lines. Its AI is trained to identify specific defects in your products, allowing for early intervention and reduced waste.

• Pros: Easy to set up, cost-effective, real-time monitoring and inspections, provides valuable data insights.
• Cons: Reliant on camera quality and placement, may require customization for highly specialized environments.

2. Amazon Recognition: Scalable Cloud-based Visual Intelligence Platform

For businesses seeking a comprehensive Visual Intelligence solution, Amazon Rekognition offers a powerful suite of tools. This cloud-based platform provides a wide range of functionalities, including object and scene detection, facial recognition, and image analysis. Rekognition’s strength lies in its scalability, making it suitable for businesses of all sizes. Whether you need to automate visual inspections on a production line or analyze customer behavior in retail stores, Rekognition can be customized to your specific needs.

• Pros: Highly scalable, vast array of functionalities, integrates with other AWS services.
• Cons: Can be complex to set up and manage, pricing structure may be unclear for smaller businesses.

3. Clarifai: Empowering Developers with a Flexible Visual Intelligence Toolkit

Developers looking to build custom Visual Intelligence applications can leverage Clarifai’s rich ecosystem of tools and pre-trained models. Clarifai offers a robust API that allows developers to integrate Visual Intelligence functionalities into existing applications. Additionally, Clarifai provides a vast library of pre-trained models that can be fine-tuned for specific tasks, such as identifying medical anomalies in X-rays or classifying objects in autonomous vehicles.

• Pros: Highly customizable, extensive pre-trained model library, developer-friendly API.
• Cons: Requires programming expertise, ongoing maintenance for custom models may be needed.

4. Omron i-AO Series: Intelligent Vision System for Automated Defect Detection

Omron’s i-AO Series offers a powerful VI solution specifically designed for quality control in manufacturing. This system utilizes high-resolution cameras and deep learning algorithms to perform automated visual inspections. The i-AO Series can be trained to identify a wide range of defects, even on complex or high-speed production lines. This system provides valuable data on defect rates and trends, allowing for continuous improvement in quality control processes.

• Pros: Highly accurate defect detection, data-driven insights for quality improvement.
• Cons: Requires huge upfront investment in hardware, may not be suitable for low-volume production runs.

5. Landing AI: Democratizing Visual Intelligence with Affordable, Easy-to-use Products

Landing AI is making VI technology accessible to a broader audience. Their Visual Intelligence tools are designed to be user-friendly and require minimal technical expertise. Landing AI offers solutions for various applications, such as visual inspection in manufacturing and automated image and video analysis. Their focus on affordability and ease of use makes Visual Intelligence a viable option for businesses of all sizes, not just large-scale enterprises.

• Pros: Affordable, user-friendly interface, minimal technical knowledge required.
• Cons: May have fewer functionalities compared to competitors, may not be suitable for highly complex tasks.

This is just a glimpse into the exciting world of Visual Intelligence AI. As technology continues to evolve, we can expect even more innovative products and solutions that will reshape how we see and interact with the visual world.

In the realm of industrial manufacturing, ensuring the quality and reliability of components is paramount. Components integral to many mechanical systems must be rigorously validated for defects such as missing handles, bolts, or screws. The challenge lies in detecting these defects efficiently and accurately. This article delves into how advanced machine learning techniques can be leveraged to implement a robust detection and alert system for identifying defective components.

About Scanflow

Scanflow for MotorGear Defect Detection is an AI-powered solution tailored for identifying defects in gearmotors within industrial settings. It employs advanced machine learning algorithms and intelligent image processing to streamline the detection process, ensuring high accuracy and efficiency.

Scanflow seamlessly integrates with existing workflows and software infrastructure, supporting a wide range of platforms and development frameworks for easy deployment. Additionally, its offline functionality enables uninterrupted operation, even in areas with limited connectivity, ensuring continuous data capture and enhancing operational efficiency.

By accurately detecting defects in gearmotors, Scanflow helps manufacturers maintain high standards of quality control, reducing manual inspection efforts and minimising production delays. Furthermore, its data security features, including encryption and offline storage, mitigate safety concerns and prevent unauthorised access, ensuring the integrity and confidentiality of sensitive information related to gearmotor inspection processes.

Objective

Our goal is to create an automated system that detects and alerts relevant personnel during the validation process of components exhibiting specific defects. This system aims to enhance accuracy, reduce manual inspection efforts, and ensure only quality-assured components proceed to the next stage of production.

Strategic Approach

• Data Collection : The foundation of any machine learning project is data. In collaboration with the client, we will gather a comprehensive dataset comprising high-resolution images of components, both defective and non-defective. This dataset will serve as the cornerstone for training and validating our model. The quality and diversity of this dataset are crucial, as they directly impact the model’s ability to generalize and perform well in real-world scenarios.
• Image Annotation : To enable our model to recognize defects, we must first annotate the images. Annotation involves labeling regions within the images that correspond to defects like missing handles, bolts, or screws. This process ensures that the model can identify these defects accurately. We will use specialized annotation tools and techniques to mark the defective regions precisely. This step is labor-intensive but essential for training a high-performing model.
• Dataset Preparation: Post-annotation, the next step is organizing the dataset. Efficient organization is crucial for training the model effectively and evaluating its performance accurately. The dataset will be split into training, validation, and test sets. The training set will be used to teach the model, the validation set will be used to tune the model parameters, and the test set will evaluate the model’s performance in unseen data. We will ensure that each set contains a balanced mix of defective and non-defective component images to prevent any bias.
• Model Training: For detecting component defects, we have created our own custom-built model. This model has been tailored specifically for our needs and is designed to accurately identify defects in components. By leveraging transfer learning, we can fine-tune a pre-trained model on our custom dataset, ensuring optimal performance. Transfer learning allows us to benefit from the knowledge gained by a model trained on a large, generic dataset, adapting it to our specific task with relatively few data.

Training Result

Training Hardware

The model training was conducted using high-performance hardware to expedite the process. This hardware configuration enabled us to train the model efficiently and effectively. The specifications of the hardware used are as follows:
Processor – Intel i7 14700KF with 28 CPUs @ 3.4 GHz
Memory – 32GB RAM
Graphics Card – NVIDIA RTX 4090

The training process took approximately 38 hours to complete in the specified hardware configuration. Utilizing such robust hardware ensures that the model training is both time-efficient and capable of handling the complexity of the task.

Real-Time Use Case

To demonstrate the model’s capabilities, we present images of a Helical Bevel Gear Motor. One image represents a defective motor, and the other represents a non-defective motor. The model’s ability to distinguish between these two scenarios is critical for practical deployment.

In the defective gear motor image, the model identifies missing handles, bolts, or screws by highlighting these areas with bounding boxes. In contrast, the non-defective gear motor image shows no highlighted regions, indicating that the component is free of defects.

Deployment and Real-Time Alerts

With the model trained and validated, the final step is deployment. Integrating the model into the component validation workflow involves several steps:

• System Integration: The model is integrated with the existing manufacturing software infrastructure. This allows for seamless interaction between the detection system and the production line.
• Continuous Monitoring: The system continuously monitors components as they pass through the validation stage. High-resolution cameras capture images, which are then processed by the model in real time.
• Real-Time Detection: The model analyzes each image, identifying any defects instantly. The system can handle a high throughput of images, ensuring that the validation process does not become a bottleneck in production.
• Automatic Alert Generation: When a defect is detected, the system generates an automatic alert. This alert is sent to the relevant personnel, such as quality control inspectors or production managers, enabling immediate action to be taken.

This integration not only streamlines the validation process but also significantly reduces the risk of defective gearmotors reaching the market. The real-time alert mechanism ensures that defects are addressed promptly, maintaining the overall quality of the components.

Conclusion

By adopting this advanced detection system, manufacturers can achieve a new level of precision in component quality control. The combination of machine learning and real-time alert mechanisms ensures that only high-quality products proceed through the production line. This approach not only enhances operational efficiency but also maintains the integrity and reliability of components.

By implementing these cutting-edge techniques, manufacturers can ensure rigorous quality control and uphold the highest standards in component production. The integration of machine learning in defect detection represents a significant step forward in industrial manufacturing, promising to improve product quality and reduce operational costs.

In summary, the advanced detection system we have developed provides a comprehensive solution to the challenge of identifying defective components in gearmotors. By leveraging the power of machine learning and high-performance hardware, we can deliver a robust and efficient system that meets the demands of modern manufacturing.

Aspect	AI Scanner	Hardware Barcode Scanner
Technology Type	Computer vision and AI-Powered scanning	Standalone hardware device for barcode scanning.
Device Requirements	Requires a smart device like smartphones, drones, wearables, or tablets with a camera.	Dedicated handheld device with a built-in scanner.
Cost	Typically involves software subscription fees; potential hardware costs.	Upfront hardware purchase cost.
Accuracy and Speed	Accurate scanning using device camera; processing speed depends on device capabilities up to 98% accuracy	Generally fast and accurate scanning performance.
Integration and Compatibility	Offers SDKs for easy integration into mobile apps; backend systems compatible with various development platforms.	Designed for specific integrations; compatibility varies.
Maintenance and Updates	Regular software updates for improvements; hardware independent.	Hardware maintenance may be required; updates depend on the manufacturer.
Adaptability to Changes	Software updates allow for adapting to new barcode/text types and features.	Hardware limitations may affect adaptability.
Flexibility	The software-based solution allows for customization and feature updates.	Limited flexibility due to hardware constraints.
User Training	Generally user-friendly with intuitive mobile interfaces.	May require training for proper use.
Scanning Range	Various CV features offer different scanning ranges.	Limited by device camera capabilities; optimal for close and medium-range scanning.
Mobility and Portability	It is highly portable due to smart device integration.	Portable, but less convenient than mobile devices.
Use Cases	Well-suited for mobile applications, retail, inventory, and logistics.	Suitable for retail, warehousing, manufacturing, and logistics.
Cost-Efficiency	Software subscriptions may offer cost savings over time.	Hardware costs can be higher upfront.

AI scanner

 
Industries rely more on AI scanners due to their flexibility, adaptability, and integration with smart devices, making them suitable for dynamic environments. Scanflow is an AI scanner that helps in effortless data capture and workflow automation. Scanflow offers a powerful solution for businesses looking to optimize their business process, improve data accuracy, and achieve operational efficiencies in a wide range of industries.

Industries require scanning solutions for workflow operations, driving visibility into resources and distribution activities. Technological innovations have created a great impact on businesses irrespective of industries. This has enhanced the business activities of small to large enterprises bringing differences in operating costs, and employee productivity. Industries like logistics, retail, and manufacturing have eased their workflow by implementing automation thus reducing manual intervention.

Smart devices are paving the way for AI’s transformation of how organizations run. The addition of AI-enabled software to smart devices such as smartphones, and wearables improves performance and perhaps has the potential to replace the need for an external device.

Software scanners are easy to adopt and use which makes them an inevitable solution to any enterprise problem associated with scanners

• How do AI scanners help industries improve efficiency?

• Quicker data capture with high-precision results
• Increases revenue while cutting down on errors.
• Cost-effective prevents loss of funds due to human error.
• High performance when compared to hardware scanners
• Reduces time in analyzing the quality of the assets, inventory, and products.
• User-friendly integration in any type of smart devices

1. List of software scanners that help in Industrial Automation

1. Scandit

 

Scandit is a technological platform for augmented reality (AR) and mobile computer vision solutions for businesses. The company’s software enables unmatched barcode scanning, text and object recognition, and real-time display for any app on any camera-equipped smart device, including smartphones, wearables, drones, and robots. Scandit is integrated into a number of partner systems, including SAP Fiori, Oracle XStore, Epic Rover, etc., and supports a wide range of hardware and software platforms.

The notable clients of Scandit include 7-Eleven, Alaska Airlines, Carrefour, Hermes, Levi Strauss & Co., Mount Sinai Hospital, Sephora, Toyota, Johns Hopkins Hospital, and La Poste.

FOUNDED: 2009
EMPLOYEES: 251–500
LOCATION: Zurich, Switzerland
FUNDING: $273.1M

2. Anyline

 

Anyline scanning SDK is used to create real-time mobile OCR apps with the highest recognition rates without the need for server infrastructure. Anyline offers a hassle-free scanning solution that will save time and money. The following industries have benefited from Anyline’s assistance in the digitization process: Utility, Government, MRO, transportation, automotive, tourism, and other product industries.

Anyline’s broad range of dependable technology can handle the most challenging business problems by combining advanced machine learning models with conditioning from the real world.

FOUNDED: 2013
EMPLOYEES: 51–100
LOCATION: Vienna, Austria
FUNDING: $37M

3. Scanflow

 

Scanflow is an AI scanner for smart devices that captures data and automate workflows. It can capture any type of data, including text, IDs, numbers, barcodes, and QR codes. Scanflow can be easily integrated with any smart device like smartphones, drones, and other wearable devices. Built with AI-powered solutions, Scanflow can scan in difficult conditions such as low-light low-light environments, long-range distances, and at any angle orientation with high precision and speed.

Scanflow enables users to experience personalized shopping, self-checkout, inventory management, and asset tracking for retail, logistics, and manufacturing industries. Scanflow-powered solutions reduce operational costs and time while boosting worker and customer satisfaction. The goal of Scanflow is to offer enterprise-grade intelligent data capture technologies that help automate workflow processes

FOUNDED: 2021
EMPLOYEES: 11–50
LOCATION: Coimbatore, India

4. Dynamsoft

 

Dynamsoft is a multinational software development company that provides SDK for document capture and barcode applications for various usage scenarios. These SDKs help developers meet document imaging, scanning, and barcode reader requirements when developing web, desktop, or mobile-based applications.

The key areas of Dynamsoft’s research and development are document imaging and barcode decoding. The SDKs are used by developers to remove the need for them to develop their own code. This removes months of work for them by cutting the need to code as well as understand relevant industry standards and requirements. Over the last 18 years, many companies across the world have used Dynamsoft’s SDK in their daily workflows to improve efficiency and reduce cost.

FOUNDED: 2003
EMPLOYEES: 51–100
LOCATION: Vancouver, Canada

5. Scanbot

    

Scanbot helps businesses all over the world in reducing the expense of human data entry. Scanbot SDK can record analog data from any mobile device. Scanbot is a mobile scanner app for documents and QR codes. Users can create premium quality PDF scans and send them through email or automatically upload them to the cloud such as Google Drive, Box, Dropbox, Evernote, and other cloud services.

Scanbot makes it possible to include scanning and data extraction features into current mobile applications for the banking, insurance, healthcare, and logistics sectors. Any enterprise application can incorporate scanning features and data extraction using SDK’s highly advanced algorithms and machine learning.

FOUNDED: 2011
EMPLOYEES: 11–50
LOCATION: Bonn, Germany

AI-based software scanners can be used by any industry to improve workflow processes and solve business-related issues. Software scanners are an essential part of asset tracking and inventory management and are being incorporated into everyday smart devices to increase data accuracy, eliminate human error, and improve business operations.

Software-based solutions are crucial for organizations that are finding ways to implement automation in their workflow processes. Intelligent data capture enables businesses to make the best possible start in establishing a better data management process. It serves as a baseline for developing an all-encompassing intelligent automation method for any enterprise.

An ideal automation approach must include intelligent data capture, which can upgrade any existing data management system. Intelligent data capture helps to categorize the type of data, extract real-time information from it, validate it for decision-making, and get stored in any POS, ERP, or enterprise system it is integrated into.

What is Intelligent Data Capture & Why it is important?

Intelligent Data Capture (IDC) is the automated process of identifying and extracting critical information from barcodes, QR codes, text, or any object without manual intervention.

By investing in Intelligent Data Capture, enterprises can save time, money, and resources by no longer having to manually extract data and organize it.

It can capture any type of data from smart devices like smartphones, drones, and wearables. Built with Computer vision and Machine learning models, intelligent data capture software has the ability to differentiate between different kinds of data classify them, and provides appropriate results so that the process becomes faster and more efficient in the long run.

Let’s take a look at the 5 Benefits of Intelligent Data Capture for any enterprise.

1. Reduces Operational Costs

The traditional data capture techniques drive up operational expenses and need extra human resources. This burden is reduced by digitizing all incoming data where fewer people are needed to manually input and verify huge datasets. This results in improved organizational growth without spending money and time on hiring more people. Intelligent capture allows workers to focus and prioritize other crucial business tasks instead of manual data processing and entry.

2. Enhanced user experience with Augmented Reality

Intelligent data capture ensures to provide real-time insights with Augmented Reality for a better user experience. It provides unique experiences by integrating the digital world with physical spaces from smart devices.

3. Improved Security & Compliance

Intelligent data capture techniques ensure that the data collected is stored only in the enterprise environment so that only users with access permissions can access it. Additionally, it allows for encrypting the data before it enters the system, protecting against expensive data loss and security breaches. This enables a business to adhere to security regulations and assures that all of its data is highly secure.

4. Increased Efficiency

Intelligent data capture allows for the quicker and error-free intake of any type of data. The efficiency of the organization as a whole is increased by removing human error from the process and giving workers the to focus on important tasks rather than manual ones. It also improves communication among remote workers by enabling dynamic contact between employees who are spread out across different locations.

5. Single-point of access

A single platform can support workers and customers in capturing data from their smart devices at any time even without internet connectivity. This reduces the learning curve for multiple software within the same business and streamlines the data gathering and verification process.

The adoption of intelligent data capture technologies is becoming more crucial in today’s world as data is increasingly becoming the aspiration of competitive advantages for enterprises.

Quick and accurate data capture is essential in fast-moving and dynamic industrial workflows. One important requirement in data capture applications is capturing text from surfaces and products, even in extreme circumstances like uneven color. While it is possible to train deep learning models for capturing text (OCR), due to the nature of deep learning models, it requires a lot of contextual data to train them from scratch.

Let’s take the example of Black on Black text which we can find in vehicle tires, like the one below.

Traditional OCR solutions fail miserably in capturing this kind of data. Training models from scratch to extract this kind of data requires a huge amount of data. Then, there are practical concerns about the data distribution of characters in the data we collect. It will be hard to collect data in a manner where each alphabet is equally distributed across the dataset. Every deep-learning engineer hates the imbalanced class problem.

And, as a deep learning engineer, if you are presented with a new complicated OCR problem, you’ll want to take advantage of unrelated larger datasets for OCR and then use them for your use case.

You would have already encountered the term transfer learning. We believe transfer learning is one of the most underrated and most important techniques in deep learning.

The core idea about transfer learning is that models have multiple layers, each layer is responsible for identifying features. The latter layers in the model build on top of the features learnt in the earlier layers. In the case of Convolutional Neural Networks, the earlier layers learning primitive features like dashes, lines and as we move to consecutive layers, the learned primitive features are then combined to detect much more complicated features.

Let’s look at an example for the above example:
The low-level features and sometimes even the mid-level level features needn’t be specific to a single task at hand but could be used across different tasks. For example, the features that define a human’s face could also be generalized across other animal species. The features of a cricket bat could be quite similar to a baseball bat. It is only in the high-level features that are learned in the latter part of the neural network would we see highly specialized task specific features.

Use of transfer learning in computer vision took off back in 2016, but its use in NLP is fairly recent with the explosion of Large Language Models (LLMs).

The idea behind transfer learning is that you train a model on a large dataset, and then use the same model which has learned the features from the large dataset, to train on smaller task-specific data. The core reason behind this is that most of the features learned for the large dataset are common across many other image recognition tasks.

This removes the need to collect huge amounts of task-specific data and reduces training time.

This has given rise to an entire research field that is known as few-shot learning.

One shot learning

One-shot learning is a very popular strategy used in facial recognition and signature-matching technologies.

The way one-shot learning works is by training a model that learns to predict the difference aka similarity score between two given inputs, be it text or images. These kinds of models don’t learn to classify images, but rather learn the features alone and then predict how different the two images are.

This way, for example in the case of facial recognition, you don’t have to train a classifier for the model to recognize each person in your organization. All you have to do is train a model on a set of paired images of people and then have the model learn their similarities or dissimilarity.

Then, all you need is a couple of images from every employee or member in your organization and that’ll be enough to identify to make predictions, irrespective of whether the model has seen images of the person.

This is why it is called one-shot learning. It is because the model doesn’t need any idea about the new people or faces that it has to classify. All you need is one sample image of the person’s face and one new real-time image from a security camera to classify that the face from the image of the camera is the same as the one from the sample image.

Few-shot learning.

Few-shot learning is about using transfer learning, but only training the model for a few epochs using less amount of data, maybe around 5 or so.

Personally, we believe that few-shot learning is among the most under-explored and underappreciated techniques in Deep Learning.

Now, how can this be used in OCR?

You can take large-scale synthetic text datasets like the Synth90K dataset and then train your recognition models on the same, which could be a CRNN model or a character recognition pipeline. This allows the pipeline to learn features specific to words and characters from the target language.

Once you train them on these synthetic datasets, you can then take the same pipeline and train them on smaller datasets that are task-specific, like the picture at the top of this post, black-on-black embedded text, which might not be properly recognized by generalized OCR solutions.

Data capture refers to the process of collecting data from various real-time sources and utilizing them for business processes. Industries have to deal with an enormous amount of data every day, creating frustration and conventional data capture tools do not satisfy the enterprise’s needs in terms of automation.

Smart scanning solutions use advanced technology, such as computer vision and artificial intelligence to enable automated scanning and data capture in various industries.

Smart data capture solutions can help to automate manual processes, reduce errors, and improve overall efficiency in various industries.

How Scanflow helps industries in workflow automation

Scanflow provides smart scanning solutions that can be used in many different applications, from retail and logistics to healthcare and manufacturing that helps in efficient workflow operations.

Manufacturing

Scanning is an inevitable practice in manufacturing as it allows workers to collect data on products, raw materials, and equipment, which can be used to improve product quality and enhance efficiency. The goods moving in and out are scanned using smart devices like smartphones, wearables, or drones.

Smart scanning in manufacturing can help to streamline these processes and make them more accurate, efficient, and cost-effective. Workers can collect data on products and raw materials without the need for manual data entry.

Scanflow barcode scanner helps warehouse workers to manage inventory stock counts and tracks components and parts in the assembly line. It is used to track inventory levels stocks easily identify when inventory needs to be replenished providing more transparency about the goods.

Logistics

Scanning enables logistics providers to capture and store data about items, which can be used to analyze performance, identify areas for improvement, and optimize operations. By scanning items, logistics providers can ensure that the right items are being shipped to the right destination. Scanflow speeds up processes like inventory management, order picking, and delivery, which can help to reduce lead times and improve customer satisfaction.

Scanflow scans tire serial numbers and container numbers that provide real-time visibility into the status of items as they move through the shipment process. This helps logistics providers to better manage inventory levels and respond to changes in demand. It plays a vital role in enabling businesses to meet the demands of a fast-paced and complex supply chain.

Healthcare

Scanflow healthcare scanning solutions are designed to improve the efficiency and accuracy of healthcare workflows, particularly in clinical settings such as hospitals and pharmacies.

It typically involves the use of smart devices, such as smartphones or tablets. Scanflow intelligent text capture helps in medication tracking be used to scan barcodes or texts on medication packages to verify the medication’s identity, expiration date, and dosage. This can help to reduce the risk of medication errors and improve patient safety.

Scanflow ID scanning can be used to scan patient ID cards to quickly and accurately identify patients and link them to their medical records. It can help to streamline workflows, reduce errors, and improve patient care.

Retail

Scanflow allows customers to use their own smart devices to scan barcodes on products as they shop, rather than relying on conventional scanners or staff. Self-scanning significantly reduces the time customers spend waiting in checkout lines, which can help to improve the overall shopping experience and increase customer satisfaction. It helps customers look for product details, reviews, offers, and discounts, reducing long queues at the billing section during check-out.

Scanflow provides customers with a more personalized and convenient shopping experience with augmented reality allowing them to shop at their own pace and avoid long checkout lines.

Most industries today are predominantly automated, and a significant portion of the industrial elite had already started in many sectors like smart manufacturing, self-retailing, or digital healthcare. The adoption of emerging Intelligent technologies for the manufacturing sector has a greater emphasis on pushing toward industrial automation. The evolution of Industry 4.0 in manufacturing connects new technology and established trends in automation and data exchange. This is possible because of the assistance provided by intelligent machines that have access to more data, where industries will be more productive, efficient, and reduced costs.

Now, let’s take a deep dive into the top 5 technologies that help in industrial automation:

1. Artificial Intelligence (AI) & Machine learning (ML)
2. Computer Vision (CV)
3. Augmented Reality (AR)
4. Natural Language Processing (NLP)
5. Optical Character Recognition (OCR)

1. Artificial Intelligence & Machine Learning

Artificial intelligence and machine learning are perhaps the two most significant technologies that come to mind when thinking about intelligent automation.AI &ML mimic how people learn by using digital data together with other components like remote inputs and algorithms. Most often, predictions are made using AI and machine learning based on analysis of historical data and past behaviors. Industrial supply chains can be optimized using AI algorithms to assist organizations in anticipating market changes. The major advantages of artificial intelligence are those related to learning and decision-making.

2. Computer Vision (CV)

The ability of computers and entire systems to glean valuable and pertinent information from digital sources is thought to be the focus of the field of computer vision. These digital sources can include different visual inputs including photographs, videos, and other visual media. On the basis of the information that has been retrieved, recommendations can be made for both more activities and broad assumptions or conclusions. Computer vision is crucial for comprehending and interpreting the visual environment as well as enabling machine interpretation in this sense. Software-based data capture tools work on computer vision algorithm that helps in accurate data capture with real-time insights.

3. Augmented Reality

Augmented reality (AR) technology overlays an image on a user’s perception of the real-time world. It combines a computer-generated virtual scene with the actual scene of the viewer. Augmented reality is a rapidly developing technology that has the potential to address significant operational issues in the industrial sectors. Workers who use AR solutions in production do action more quickly. Field service technicians and remote specialists can communicate with each other in two directions using AR solutions. This technology has the ability to disrupt the manufacturing sector and make it more adaptable, efficient, and customer-focused.

4. Natural Language Processing (NLP)

Natural language processing also called NLP, is a subfield of artificial intelligence. NLP focuses on how computers and humans interact and relate to one another. This technology recognizes the important components of human instructions, extracts pertinent information, and then processes the information to allow robots to understand it. The adoption of NLP in the manufacturing process reduces repetitive tasks, ensures smooth automation without any interruption, and frees up workers from activities that call for human skill sets.

5. Optical Character Recognition (OCR)

Optical character recognition also known as text recognition is a process that converts handwritten or printed text images into machine-encoded text. In manufacturing industries, the batch ID, lot code, and expiration date are crucial data to be collected. Workers rely on manually entering each entry individually which requires a lot of time and work. The use of OCR technology could reduce the effort by extracting the data from the text, which can be stored in a smart database.

In order to gain a competitive advantage, industries require early adoption of new prospects and developing technologies into their workflows. Industries like manufacturing, healthcare, energy, and finance are gaining benefits from technological advancements like Artificial intelligence, virtual reality, process intelligence tools, and 3D visualization. This increases success rates through a more efficient and productive work environment.