Author: scanflow

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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.

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.

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.

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)

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.

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.

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.

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.

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.

Have you ever imagined your smart device fulfilling all your scanning needs?

Smart scanning solutions are incrementally being adopted by industries. It helps enterprises in real-time decision-making and workflow automation. The users can access data using a single camera-equipped smart device. When compared to conventional data capture methods, smart data capture has many benefits which include accurate data, quicker response times, increased security, validation checks, and the ability to detect problems that humans might miss.

Smart data capture uses mobile devices like smartphones, tablets, wearables, and drones to capture and process data conveniently. The data capture process can be carried out from the already familiar device where workers do not need to get special training in using other external data capture devices.

Smart data capture empowers industries where a large workforce is involved. It is difficult to provide each worker with an external data capture tool and manually enter it. Instead, they can do the scanning from their own smart devices. The data can be extracted with a single scan using a smart device.

Scanflow is an AI scanner on smart devices for data capture and workflow automation. It captures any type of text, IDs, serial numbers, barcodes, and QR codes under any challenging conditions. It can be integrated into any development framework as mobile and web SDKs deliver instant and accurate scanning results. Scanflow has the capability of providing more than one scanning solution in a single smart device.

Scanflow Intelligent text scanning is used to capture and recognize text from images or objects which is designed to be highly accurate and flexible and can recognize text in various languages, font styles, and sizes. It can capture alphanumeric texts from tires, containers, or any other

Scanflow ID scanning transforms any smart device into an enterprise-grade scanner. It captures data from ID cards, passports, and driver’s licenses.​Scanflow can be used for KYC verification in the banking & financial sectors, passport scanning in airlines, and patient onboarding in healthcare. Scanflow ID scanning SDK is available in both mobile and web apps based on the enterprise’s needs.​

Scanflow decodes any type of 1D / 2D barcode symbologies that can be integrated into both mobile apps & web SDK​. It scans with high speed, accuracy, and consistency. It helps manufacturing industries in counting stocks, inventory management, assembly line operations, and managing overall warehouse operations from a single device. Workers can collect data remotely through their own devices which improves their productivity.

• Benefits when using a smart data capture solution:

• Eliminates manual entry of data which reduces human errors.
• Enhanced employee engagement as workers can focus on other priority tasks
• Improves operational efficiency by streamlining the workflow process
• Enhanced user experience through flexibility and convenience
• Smart devices are easy to access from anywhere at any time

Smart data capture from a single device helps industries reduce their reliance on people and facilitate the smooth running of business processes. Computer vision technology has dramatically improved how data is captured, processed, and stored in the enterprise environment. Such digital innovations in industries promise a productive workforce, efficient operations, and an increase in ROI.

In today’s rapidly evolving digital landscape, data capture and identification technologies play a crucial role in streamlining processes, improving efficiency, and enhancing customer experiences. While barcodes have been the go-to method for data capture for decades, the future holds a plethora of exciting advancements that go beyond traditional barcodes. In this blog, we will explore some of the cutting-edge technologies, including Augmented Reality (AR) and Text Scanning, that are shaping the future of data capture and identification.

Computer vision and image recognition technologies leverage artificial intelligence and machine learning to interpret visual information from images and videos. These technologies can identify and recognize objects, patterns, and even human emotions. In data capture, image recognition can automatically extract data from images, making it a valuable tool for industries dealing with large volumes of visual information, such as insurance claims processing, medical imaging, and document digitization.

Augmented Reality (AR) has taken the tech world by storm, transforming how we interact with the digital and physical worlds. AR overlays digital information onto the real environment, enhancing users’ experiences and providing valuable insights. In data capture, AR can be utilized for interactive identification and information retrieval. For instance, AR-enabled smart glasses or smartphone apps can recognize objects or products and display relevant data, such as specifications, reviews, or pricing information. This technology is particularly valuable in retail, where customers can make informed decisions through real-time product information.

AR also holds potential in industrial settings, allowing workers to identify and access critical information about machinery, maintenance procedures, or safety guidelines, simply by pointing their devices at specific components or equipment. The immersive and interactive nature of AR-based data capture enhances accuracy, efficiency, and engagement in various industries.

Intelligent text scanning that enables instantaneous data extraction from texts, documents, images or videos. This technology can recognize and process text in real-time, making it ideal for applications where speed and efficiency are paramount.

In logistics and transportation, it instantly captures shipping labels, tracking numbers, or product information from packages, facilitating seamless tracking and inventory management. In healthcare we can quickly extract patient data from medical records, capture medical REF codes, enabling healthcare professionals to access critical information at the point of care.

Artificial Intelligence (AI) and Machine Learning (ML) have been advancing data capture and identification technologies. ML algorithms enable systems to learn from vast amounts of data and continuously improve their accuracy and performance.

In data capture applications, AI-powered OCR systems can recognize various fonts, languages, and handwriting styles with remarkable precision. Machine learning algorithms can also be employed to fine-tune models for specific industries or use cases, enhancing the accuracy of text extraction.

Furthermore, AI-driven image recognition and object detection are transforming data capture for non-textual information. These technologies can identify and extract data from images, such as product codes, serial numbers, or even facial recognition for identity verification.

The convergence of these cutting-edge technologies will revolutionize data capture and identification across various industries, ranging from retail and healthcare to logistics and finance. By embracing these advancements, businesses will be empowered to make data-driven decisions, provide personalized customer experiences, and optimize their operations for a smarter and more connected future.

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