Category: general

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.

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.

How Scanflow efficiently captures data from a single 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.

Intelligent Text scanning

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

ID scanning

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.​

Barcode/QR code scanning

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:

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) for Interactive Data Capture:

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

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

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.

Get in touch with us to know more on Scanning Technologies: ScanFlow – AI Scanner on Smart Devices for Data capture and Workflow Automation

When it comes to barcodes, there are various types available, each with its own unique features, opportunities, and limitations. Understanding the differences between 1D and 2D barcodes, as well as the major barcode variants, can help you choose the most suitable option for your industry and maximize efficiency and business value.

Let’s delve into the details:

1D Barcodes (Linear Barcodes):

• Commonly used codes: UPC, EAN, Code 39, Code 128.
• Common uses: Retail product labeling, inventory management, tracking assets.
• Limitations: Can only store limited information (e.g., product identification numbers), not suitable for complex data or extensive information storage.

Now, let’s explore the major 1D barcode types:

1D Barcode Types:

• UPC (Universal Product Code): Widely used in retail for product identification.
• EAN (International Article Number): UPC, used globally for product identification.
• Code 39: Supports alphanumeric characters and is popular in logistics and inventory management
• Code 128: Offers high-density data storage and is commonly used in shipping and product labeling.

2D Barcodes:

• Commonly used codes: QR Code, Data Matrix, PDF417.
• Common uses: Mobile marketing, ticketing, product tracking, document management.
• Advantages: Can store large amounts of data, including text, numbers, and even multimedia content.
• Limitations: Requires specialized 2D barcode scanners or mobile devices with built-in scanning capabilities.

Now, let’s explore the major 2D barcode types:

2D Barcode Types:

• QR Code (Quick Response Code): Versatile and widely adopted for various applications, including marketing, ticketing, and inventory tracking.
• Data Matrix: Highly compact, ideal for small surfaces, and commonly used in electronics and manufacturing.
• PDF417: Stores large amounts of data, commonly used in ID cards, driver’s licenses, and shipping labels.
• Aztec Code: Compact and robust, often used in transportation and ticketing systems.
• MaxiCode: Designed for high-speed scanning and used in logistics and package delivery services.

These are just a few examples of the major barcode types available. Each variant has specific strengths and limitations, making it important to select the barcode that aligns with your industry’s requirements and use cases. Consider factors such as data capacity, readability, scanning equipment availability, and compatibility with existing systems.

By understanding the differences and capabilities of various barcode types, you can make an informed decision and leverage barcodes to enhance efficiency, streamline operations, and maximize the value for your enterprise.

Software scanners digitize physical documents and images, converting them into electronic formats that can be easily stored, managed, and accessed digitally. These software scanners leverage computer vision technology, image processing algorithms and other advanced features of machine learning to enhance the accuracy and efficiency of document capture. High accuracy and efficiency in software scanning is crucial for businesses seeking to streamline their data capture processes and improve overall productivity. Whether it’s text scanning, document scanning, or barcode recognition, following these best practices can significantly enhance the accuracy and efficiency of software scanning.

1. Choose the Right Software:

Selecting the right scanning software is the first step towards achieving high accuracy and efficiency. Look for software that suits your specific needs, whether it’s extracting text, document management software, or barcode recognition tools. Consider factors like the software’s compatibility with your existing systems, scalability, and support for different file formats.

2. Optimize Image Quality:

The accuracy of software scanning heavily relies on the quality of the input images. Ensure that the scanned documents or images are of high resolution, properly aligned, and free from any distortions or shadows.

3. Use Predefined Templates and Validation Rules:

By defining validation rules, you can ensure that the extracted data meets certain criteria, reducing the chances of errors and minimizing the need for manual verification.

4. Train and Fine-Tune OCR Models:

If you have OCR software that supports machine learning capabilities, consider training and fine-tuning the OCR models with relevant datasets. By training the software on data specific to your domain and language, you can improve the accuracy of text extraction, especially for fonts and languages that may not be included in the default models.

5. Implement Automated Data Verification:

To enhance accuracy, implement automated data verification processes. Use algorithms or validation rules to cross-check the extracted data against existing databases or known values. Automated verification helps identify and flag potential errors, allowing for swift corrections and minimizing manual intervention.

6. Batch Processing

Leverage batch processing capabilities of scanning software to handle large volumes of documents efficiently. This allows you to process multiple documents simultaneously, reducing processing time and increasing overall efficiency.

7. Regular Maintenance and Updates:

Keep your scanning software up to date with the latest patches and updates to benefit from improvements in accuracy and performance. Additionally, regular maintenance of the software and hardware ensures that they function optimally, preventing potential issues that could impact accuracy and efficiency.

8. Continuous Training and User Education:

Invest in continuous training for users to ensure they are proficient in using the scanning software effectively. Educate them about best practices and how to optimize scanning processes to maximize accuracy and efficiency.

By following these best practices, businesses can achieve high accuracy and efficiency in software scanning. From selecting the right software and optimizing image quality to implementing automated verification and continuous user training, each step contributes to streamlining data capture processes and improving overall productivity. With accurate and efficient scanning, organizations can make better-informed decisions, enhance data integrity, and stay ahead in today’s data-driven world.

Empower your business with smart data capture – Connect with us today! ScanFlow – AI Scanner on Smart Devices for Data capture and Workflow Automation

In the fast-paced digital era, businesses are constantly seeking innovative ways to streamline their processes and boost productivity. Data capture has witnessed remarkable progress with automation technologies. The days of manual data entry and time-consuming processes have gone and today, cutting-edge advancements have revolutionized data capture, significantly reduced errors and enhancing efficiency.

Let’s now explore five key advancements in scanning and automation technologies that have transformed the way businesses handle data.

Barcode and QR Code scanning

The invention of barcodes revolutionized various industries, offering an efficient and standardized way to encode information on products and packages. Barcodes have become an integral part of retail, logistics, transport, manufacturing, healthcare, and numerous other sectors. However, advancements in technology have led to smarter barcode scanning applications that have completely transformed data capture processes, making them faster, more accurate, and highly efficient.

Moreover, some advanced smart scanning applications incorporate augmented reality (AR) features. These AR capabilities allow users to overlay digital information, such as SKU-level insights or real-time data, directly on the device screen. For example, when scanning a product, the application can display detailed information about that item, including its price, stock level, and even related product recommendations. This real-time data empowers users to make informed decisions quickly and efficiently.

Smart barcode scanning applications are their ability to enhance picking processes in warehouses and distribution centers. With these applications, workers can instantly pick a single product out of many without the need for laborious manual scanning or searching through shelves. This improves order accuracy, reduces fulfillment time, and boosts customer satisfaction.

From retail to logistics and manufacturing to healthcare, the impact of these applications is felt across various industries, streamlining workflows and enhancing operational efficiency. As technology continues to advance, we can expect even smarter and more sophisticated barcode scanning solutions to further transform the way businesses handle data capture and information management.

ID Scanning

ID scanning software is a powerful tool that automates the process of capturing and extracting data from various identification documents, such as driver’s licenses, passports, ID cards, and visas. This software leverages advanced technologies like Optical Character Recognition (OCR), Machine Learning, and Natural Language Processing (NLP) to accurately recognize and extract information from these documents.

Document Recognition: ID scanning software is designed to recognize different types of identification documents, ensuring it can handle a wide range of formats and layouts.

Age Verification: For businesses that require age verification for age-restricted products or services, ID scanning software can automatically validate the age of the individual against the legal criteria.

Expiry Date Check: The software checks the expiry date of the ID document, ensuring that it is valid and not expired.
Cross-Checking Data: ID scanning software can cross-check the extracted data against other databases or watchlists to identify potential risks or flagged individuals

Applications of ID Scanning Software:

Age-Restricted Sales: Retailers and businesses selling age-restricted products like alcohol, tobacco, or adult content use ID scanning software to verify the age of customers before making a purchase.

Financial Institutions: Banks and financial institutions use ID scanning software to verify customer identities during account openings and transactions to prevent fraud.

Healthcare: Healthcare providers can use ID scanning software to verify patient identities during check-ins and appointments, ensuring accurate medical records and reducing errors.

ID scanning software automates the data capture process, reducing the need for manual data entry and significantly speeding up identity verification and document process.The streamlined verification process with ID scanning software results in a smoother and more seamless customer experience, leading to increased customer satisfaction.

Optical Character Recognition (OCR)

Optical Character Recognition (OCR) has been around for some time, but recent advancements have taken it to new heights. OCR technology can now accurately recognize printed and even handwritten text, converting scanned documents into editable and searchable digital formats. Thanks to machine learning algorithms, OCR software can adapt to different fonts, languages, and writing styles, increasing accuracy and reducing the need for manual intervention. This advancement has greatly improved data extraction from invoices, receipts, forms, and various other documents, saving organizations valuable time and resources.

Multilingual Support: OCR has expanded its language support, catering to a vast array of languages from around the world. With advancements in character recognition techniques and language modeling, can accurately process documents written in various scripts, including Latin, Cyrillic, Chinese, Japanese, Arabic, and more. This multilingual support is invaluable for businesses and organizations operating globally, facilitating seamless data capture and communication across different languages.

Real-time OCR on Mobile Devices: The proliferation of smartphones and tablets has led to a demand for real-time OCR capabilities on mobile devices. Advances in mobile computing power and OCR algorithms have made it possible for OCR applications to run directly on mobile devices, allowing users to instantly extract text from images taken with their device’s camera.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of AI that enables machines to comprehend and analyze human language. NLP has opened new avenues for data capture by allowing systems to extract information from unstructured text sources like emails, chat logs, and social media posts. By using NLP algorithms, businesses can collect valuable insights from customer interactions, feedback, and market trends. This technology empowers companies to make data-driven decisions and gain a competitive edge.

The continuous advancements in scanning and automation technologies have reshaped data capture processes and propelled businesses toward greater efficiency and accuracy. By embracing these innovations, it minimizes manual errors and improves data quality but also empowers organizations to focus on strategic decision-making and customer-centric initiatives. As technology continues to evolve, further enhancements in data capture will emerge, simplifying complex workflows and propelling businesses into a brighter, data-driven future.

Empower your business with smart data capture – Connect with us today! ScanFlow – AI Scanner on Smart Devices for Data capture and Workflow Automation

In the era of digital transformation, businesses are generating an unprecedented volume of data everyday. Data capturing, processing, and analyzing it efficiently is critical for organizations seeking to make informed decisions, enhance operational efficiency, and gain a competitive edge. Artificial Intelligence (AI) has emerged as a game-changing technology in the realm of smart data capture, revolutionizing how businesses handle and leverage information.

What is Smart Data Capture?

Smart data capture is the process of automatically extracting and digitizing data from various sources, including documents, images, videos, and audio. Traditional data capture methods often involve manual entry, which can be time-consuming, error-prone, and resource-intensive. Smart data capture, powered by AI and machine learning, automates this process, enabling organizations to capture and process vast amounts of data accurately and efficiently.

The Role of Artificial Intelligence in Smart Data Capture

• Optical Character Recognition (OCR): AI-driven OCR technology has transformed the way data is extracted from scanned documents. Traditional OCR methods were limited to recognizing printed text, but AI-powered OCR can now accurately interpret handwritten text, different languages, and various fonts. This advancement significantly improves data accuracy and eliminates the need for manual intervention in data entry.
• Natural Language Processing (NLP): Unstructured data, such as emails, social media posts, and customer feedback, poses a challenge for traditional data capture techniques. NLP, a branch of AI, enables systems to understand and interpret human language, extracting valuable insights from unstructured data sources. This capability allows businesses to gain deeper insights and make data-driven decisions
• Intelligent Document Recognition (IDR): AI-driven IDR combines OCR with machine learning algorithms to intelligently analyze and extract data from documents with varied formats and layouts. The software can recognize patterns and context to accurately capture relevant information, enhancing data accuracy and accelerating document processing.
• Image and Video Analysis: AI-powered image and video analysis tools have transformed data capture from visual sources. These tools can identify and extract information from images and videos, enabling applications in fields like healthcare medical image analysis and security, e.g., facial recognition

Benefits of AI in Smart Data Capture

• Enhanced Accuracy: AI continuously learns from data patterns, resulting in improved accuracy over time. Smart data capture systems can achieve high levels of accuracy, reducing errors and minimizing the need for manual data validation.
• Increased Efficiency: Automating data capture processes with AI significantly speeds up operations. By handling large volumes of data quickly and accurately, organizations can streamline workflows, save time, and allocate resources more efficiently.
• Cost Savings: Smart data capture eliminates the costs associated with manual data entry and processing errors. By reducing the need for human intervention, organizations can optimize labor resources and allocate them to higher-value tasks.
• Scalability: AI-powered data capture solutions are highly scalable, capable of handling large data volumes without compromising performance. This flexibility allows businesses to adapt to changing data requirements and growth.

Impact of AI in Various Industries

• Finance and Banking: AI-driven data capture streamlines processes like invoice processing, loan application evaluation, and fraud detection. With automated data extraction, financial institutions can improve customer experiences and reduce processing times.
• Healthcare: AI-powered image and text analysis enhance medical diagnostics and streamline patient records management. Smart data capture enables faster and more accurate processing of medical information, ultimately leading to better patient outcomes.
• Logistics and Transportation: AI-powered smart data capture expedites cargo and shipment handling, optimizing logistics processes and ensuring timely deliveries.
• Retail and E-commerce: AI-driven data capture optimizes inventory management, supply chain operations, and customer interactions. By capturing and analyzing customer data, businesses can personalize customer experience and improve customer engagement

Artificial Intelligence has become a driving force in smart data capture, revolutionizing how businesses collect, process, and leverage information from various sources. AI-powered data capture systems are capable of handling vast amounts of data with high accuracy and efficiency. The benefits of AI in smart data capture extend to various industries, including finance, healthcare, retail, logistics, and more. As technology continues to evolve, AI is expected to play an increasingly significant role in shaping the future of data capture, enabling businesses to unlock valuable insights, make informed decisions, and remain competitive in the data-driven era.

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