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.

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.

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.