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The interest in optical character recognition (OCR) and intelligent character recognition (ICR) technology is falling (see figure 1) as companies switch to more automated solutions, such as machine learning-enabled data extraction. However, due to its various benefits, many companies still use1 or plan to use tools powered by OCR technology in their paper-based operations.
Whether you use OCR/ICR or data extraction tools enabled with machine learning, you require training data to develop robust models for such solutions. Preparing datasets to train such models can be challenging.
Therefore, this article explains how developers and business leaders can prepare effective datasets to streamline the development and implementation process of their intelligent document processing (IDP) solutions.Figure 1. Interest in OCR and ICR technology
Source: Google trends1. Define the purpose of the dataset
First establish the dataset’s purpose. This will make it easier to decide what kind of data needs to be gathered and how it should be presented.
For instance, if the dataset’s goal is to train an OCR system to recognize text in scanned paper-based or digital documents, the information gathered should include scanned images of text in a range of font sizes, styles, and arrangements. On the other hand, if the system needs to scan documents like invoices or bills, then the dataset should include images of numerical values, calculations, formulas, etc. (see image below).
The following image shows an example of an OCR system identifying numerical values in an invoice through bounding box tags:2. Collect relevant data
Once the purpose of the dataset is understood, the next step is to collect the relevant data. This can be done by using the following data collection methods:
To learn more about these data collection methods, check out this article.
It’s critical to gather data that is representative of the kinds of documents the system will be handling. For instance, for an AI-powered resume screening system, you need to gather data that contains images of different types of resumes, such as:
Format (Chronological, functional, or combination)
Academic or professional
Field-specific resumes (For instance, a resume for a software developer will contain different terminologies as compared to a resume of a human resource candidate)
Similarly, images of handwritten text may be required for a system that will scan handwritten documents like letters or forms. The more diverse the dataset is in terms of variations in writing tools, content, writing styles, and other factors, the better the OCR system will function on new, unseen images.Figure 2. Shows the process happening under the hood
In another example, a license plate recognition system also uses OCR technology. The data that is required to train such systems is usually blurry images of different types of license plates in different angles and different lighting scenarios. This is mainly because the system usually needs to scan fast-moving vehicles.Our recommendations
If preparing your own dataset through in-house data collection does not suit your project timeline or budget, you can consider outsourcing or crowdsourcing the data.3. Annotate the data accurately
Data annotation is a crucial step in preparing training data for any machine learning model, and so is the case for OCR processing. This involves modifying the data through labels and tags to make it easier for the system to recognize the text and the data that needs to be extracted.
Things to consider while annotating OCR and ICR systems:
In the case of an OCR system, the data should be labeled with the text that appears on the input image.
On the other hand, for an ICR system, the data should be annotated with the information that is attached to each unit of text/numerical value (e.g., date, amount, etc.).
For higher quality annotation, you can rely on a validator for important annotation work that will double check the annotation work done by the first annotator.
Data annotation can be done manually by human annotators or by using semi-automated tools.Manual annotation
In manual annotation, human annotators can label the images with the corresponding text using tools like a text editor, a graphical user interface, or specialized annotation software. This process can be time-consuming and may require multiple annotators for large-scale datasets.Semi-automated annotation
Leveraging semi-automated tools can fasten the annotation process by providing assistance by using OCR for handwriting recognition algorithms. These tools can automatically create text transcriptions, which can then be reviewed and corrected by human annotators. This human-in-the-loop approach can significantly reduce the amount of time required for manual annotation while ensuring the quality of the data.Our recommendations
Regardless of the method applied, it is important to make sure that the annotated data is accurate and consistent throughout the dataset. If in-house data labeling and automated tools do not suit your project requirements, then you can work with a data labeling partner.
If you are having trouble finding the right data labeling service, check our data-driven guide to selecting the right data labeling partner for your project.4. Split the dataset into training, validation, & test sets
Once the annotation is done, the dataset can now be divided into training, validation, and test sets.
The training set is used to train the model.
The validation set is used to evaluate the performance of the model during training.
The test set is used to evaluate the performance of the model after the training phase is complete (Metrics such as character error rate and word error rate are used with this subset of data to evaluate the output of the model).
It’s important to make sure that the 3 subsets accurately represent the data that the system will be processing. This can be done by randomly sampling the data to ensure that each set contains a similar distribution of data.
Check out this quick read to learn more about the AI training process.5. Preprocess the data
The data should be pre-processed to ensure that it is in the correct format and has the desired quality for training before being fed into an OCR or ICR system. Pre-processing can help to reduce or eliminate noise sources, enhance the quality of the data as a whole, and boost the system’s accuracy.
For instance, consider a scenario where the OCR system is trained to recognize handwritten text. If the input data is not pre-processed, it may contain a lot of noise, such as smudges, creases, and distortions. This noise can make the recognition process more challenging. On the other hand, if the input data has been processed to eliminate noise and improve the quality, the OCR system will be more likely to recognize the text accurately.
To learn more, check out our article on the 5 steps to data preprocessingConclusion
Preparing an effective dataset is crucial for training any intelligent document processing system fitted with OCR/ICR technology. By considering the best practices outlined in this article, you can ensure that the dataset covers all the features of the model and helps the system achieve the desired results.
To learn more about training data collection for AI/ML models, download our free whitepaper:
You can also check our data-driven list of data collection/harvesting services to find the best option for your project’s data needs.Further reading
If you need help finding a vendor or have any questions, feel free to contact us:References
Slotta, D. (Jul 30, 2023) “Market share of optical character recognition among image recognition technology in China from 2014 to 2025” Statista. Accessed: Feb 02, 2023.
Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.
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The use of online surveying is increasing, and research shows that the online survey market is expected to reach almost $14 billion by 2026. Having the right respondents is crucial for online surveys. However, data without a proper analysis may not tell a lot, is hard to interpret, and can lead you in the wrong direction. That’s why analyzing your data is as important as getting it.
Even though the quality of your data analysis depends mainly on your dataset and question types, there are some essential steps in conducting a proper analysis to get the best from your survey data. Here we focus specifically on online surveys and provide guidelines for getting best insights from your data through online survey analytics.What are the types of data to be analyzed?
There are different data types, each with unique analysis methods. For instance, if you ask people where they are from, you cannot just rank them because no country has priority over others. Thus, you need to find a different way of interpreting these data types.
In general, there are two different types of data: qualitative and quantitative.Qualitative
You can collect qualitative data to understand “why” or “how” a phenomenon occurs. For instance, when you want to understand why some customers do not prefer talking to chatbots, you can understand it by asking questions that require descriptions or explanations. Qualitative data is also called descriptive data and is usually represented by nouns, names, written content, and audio or video recordings.Quantitative
Quantitative data refers to numbers, something that can be quantified and measured. Through quantitative data, you can understand “how much,” “how often,” “how likely,” or “when” a phenomenon occurs. Thus, it is an essential data type to understand the patterns of customers, the market, or the public in general.What are the essential steps in conducting survey analysis? 1. Understand types of data you collected and the levels of measurement
As mentioned above, each data type requires a different analysis method. Thus, first, you need to identify your questions based on their levels of measurement.Figure 1. The levels of measurement 2. Start with the less complicated data type
Qualitative data contains language and is harder to categorize correctly, so you may not get useful insights unless you conduct deep analysis. Thus, starting with quantitative data is better, as it is already structured and faster to analyze.3. Determine a framework for qualitative data
As you deal with a bunch of words, you need a solid framework to analyze your data. The framework usually creates a coding scheme so that you can label your data and better understand the results.4. (Optional but highly recommended): Implement AI techniques to analyze qualitative data
As manually labeling and analyzing the data requires great human effort, you can also implement AI-powered solutions. Natural Language Processing (NLP), for instance, is a method that enables machines to categorize texts or speeches and conduct an analysis much faster than humans.
You can learn more about data labeling with NLP by reading our comprehensive article.5. Visualize your data
Although numbers are powerful in showing the relationship between different constructs, it may be hard to understand them at first glance. Thus, visualizing your data analysis can help you comprehend your findings better.Figure 2. Data visualization techniques suitable for different measurement levels
To learn more, read our guide on data visualization.
If interested, here is our data-driven list of survey participant recruitment services.
Reach us if you have any questions about conducting research surveys:
Begüm is an Industry Analyst at AIMultiple. She holds a bachelor’s degree from Bogazici University and specializes in sentiment analysis, survey research, and content writing services.
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blog / Data Science and Analytics 5 Ways to Boost Your Data Analyst Career With a Training Program
The global big data analytics market is expected to reach $745.15 billion by 2030. That would be a massive increase from the $307.52 billion that it is currently. This exponential growth can be attributed to businesses’ increased use of digital solutions for marketing opportunities, growth in databases, and the rise in edge computing technology. Consequently, as the market grows, the need for data analysis professionals is also expected to rise significantly. Hence, now is the best time to enter the field of data analytics. This blog discusses how to transition into a data analytics career and the benefits of a data analyst training program in making that transition smooth and easy.Benefits of Transitioning to a Career in Data Analytics
Before discussing how to grow a career in data analytics, let’s see the relevance of a data analytics career today. The following are some benefits of transitioning to a career in data analytics.1. A Future-Proof Career
According to the World Economic Forum’s Future of Jobs Report 2023, 80% of organizations are likely to adopt big data analytics technology by 2027. This will make data analytics the third most popular technology in the digital world. Hence, transitioning a career to data analytics offers job security and a secured career path due to many opportunities.2. Increasing Options 3. High Earning Opportunities
Data analytics is also among the highest-paying tech jobs globally. Beginner data analysts in the U.S. earn an average salary of $83,067 per year. Moreover, experienced data analysts can earn an annual average salary of $126,442.4. A Challenging Work Environment
Collecting, processing, and analyzing large volumes of data to extract valuable insights for business is a tough job. Therefore, being a data analyst is undoubtedly a challenging role. However, it also helps businesses succeed through innovation and strategic decision-making. Thus, a career in data analytics is best for people who love to take on new challenges and seek career satisfaction.How Can an Online Data Analyst Training Program Help Professionals Make a Career Switch?
Before transitioning to a career in data analytics, it is essential to understand that there is a significant gap between academia and the job market in the data analytics industry. This is because of the interdisciplinary nature of data science and data analytics, which makes it difficult to create a uniform skill set. Data science professionals have a broader knowledge of data analytics and other skills. However, data analytics is a whole skill within the umbrella of data science that requires expert training. A data analyst training program provides this expert training.
Let’s understand how an online data analyst training program helps transition careers:1. Skill Development
Having the most coveted skills is crucial when switching careers to data analytics. It makes navigating a new field easier, and these skills can also make up for the lack of experience in data analytics. Online data analyst training programs teach various skills such as data manipulation, statistical analysis, data visualization, programming languages (Python or R), SQL (Structured Query Language), and more.2. Flexibility
Switching to data analytics from other professions requires strategic career planning. Therefore, gaining data analytics skills based on career goals is necessary before making a switch. An online program can help professionals learn data analyst skills while pursuing their jobs. It also offers the flexibility to learn about data science without displaying a career gap on your CV.3. Learning From Leading Global Institutions
Data analyst training programs are offered in partnership with leading universities or colleges offering data analytics courses. These courses offer insights into the global data science and analytics industry. A program thus prepares learners eager to join a global workforce.4. Certifications 5. Guidance
Most data analyst training programs have a dedicated mentor or career guide who helps learners smoothly switch to a career in data science. They offer job assistance and help with interview preparations too.
ALSO READ: How a Data Analytics Course Can Change How You Work Forever!What Skills do Professionals Need to Succeed in Data Analytics?
Here are 10 popular skills one needs to build a successful career in data analytics:
Data analysis to gain insights
Probability and statistics to collect, analyze, and interpret data to find the probability of future events or forecast future trends
Data management, which involves collecting, storing, processing, and interpreting data through secured means
Machine learning to develop models that can draw insights from data patterns
Mining to help find hidden or uncovered data patterns from large databases
Data visualization, which is representing data insights into a visual format for a better understanding
Learning SQL, a programming language used for processing large databases
Data modeling involves creating data models or diagrams to define data flow. This is used for data formatting
Statistical programming to create
statistical programs or models for interpreting data
Finally, cultivating business intelligence, which means analyzing data and interpreting it to make sound business decisions
ALSO READ: How to Build a Successful Career in Data Science and Analytics?How Long do Online Data Analytics Training Programs Typically Take to Complete? Why are Emeritus Data Science Courses in Demand for Career Transitioning?
Career transitioning has become a common phenomenon in the past few years. Some of the main reasons for switching careers are better-paying opportunities, satisfaction, intellectual growth, and finding jobs that align with one’s values. Consequently, more people are exploring different occupations, labor markets, and function areas for a fulfilling career.
Nevertheless, despite the increase in career transition, it has various challenges or limitations—such as a lack of resources, guidance, and skill gaps. Emeritus’ online data analyst training program helps professionals overcome these challenges in the following ways:Skill-Based Learning
Emeritus’ courses focus on outcome-oriented training. That means they teach practical skills like business intelligence, data visualization, and data analysis.Networking
Since industry experts teach our data analyst training programs, they help learners build a professional network. Learners can seek guidance from the network to grow their careers in data analytics.Practical Projects
Emeritus’ data analyst training program also requires learners to apply their data analyst skills to real-life projects and case studies.
By Sneha Chugh
Write to us at [email protected]
Most of us have connected a single monitor to a laptop for extending our working space. Just like that, attaching dual, triple, or multiple screens is also extremely easy!
But before moving forward, you need to check the compatible ports and adjust the screen positions. Also, it’s important to configure the projection settings once you’ve successfully established the connection. Without further delay, let’s jump into the step-by-step guide on how to connect two monitors to a laptop.
Check compatible monitor ports in your laptop
Firstly, it’s essential to know how you will connect the two monitors to your laptop. You may directly connect to the graphics ports, daisy chain the displays, or use a docking station/adapter.
Most laptops come with just one or two output monitor ports. Among them, HDMI and USB-C are the most common interfaces. However, if you have installed a discrete graphics card, you’ll get a few more options.
If your laptop supports two or more monitor connections, you can directly attach them using the right cables and adapters. But before that, kindly check whether the monitor is compatible with the output ports on your GPU.
Check if monitor is compatible with your laptop’s ports
For daisy chaining, ensure the laptop and primary display supports DisplayPort 1.2 or Thunderbolt output ports (with MST). For a docking station, the system requires a DisplayPort over USB-C (3.1) interface.
Lastly, HDMI splitters are just for duplicating screens. Thus, if you’re opting to extend the display, it’s best to get an appropriate adapter or docking station. For example, you can use a USB-to-HDMI adapter to attach one monitor and directly join another through HDMI if your output device only supports HDMI.
Note: Since Miracast only lets you connect one additional monitor, it isn’t feasible for joining multiple screens. Instead, you need to join one display wirelessly and another using a compatible cable. Otherwise, you may pick a desirable third-party application for mirroring your notebook computer.
Place your laptop in middle and monitors in the side
The second step is to position your laptop and monitors. While this is entirely based on your preference, we recommend keeping the primary screen (laptop) in the middle for better viewing and ergonomics.
You may even opt for keeping the laptop on the left-most side followed by the two external displays. This can come in handy when you want to close the lid and continue working on the dual monitor setup.
Close you laptop and use dual monitors
When talking about ergonomics, we even recommend using a laptop stand and monitor arms. This helps save your desk space and even adds aesthetics to the setup.
Once you’re done adjusting the devices, it’s time to connect the primary monitor to the laptop. You may first extend your ports using a docking station or directly plug the cable into the notebook computer’s interface. For demonstration, we have used an HDMI cable to connect the laptop to the primary screen:
Get ready with an HDMI cable and ensure the ports are clean.
The secondary monitor connection can be a little different based on your choice and compatibility. In this section, we have utilized a USB-to-HDMI adapter to connect it to the secondary display. If your monitor supports a USB-C interface, you may directly attach the secondary monitor using a double-ended USB-C cable.
Note: In a daisy-chain setup, directly connect the DisplayPort or Thunderbolt cable to the secondary screen. If you’re using a docking station to connect dual monitors, you need an appropriate interface that is compatible with the secondary display.
Once you’ve properly attached the two monitors to a laptop, the final step is to check whether the connection is successful. Also, you may require tweaking the projection settings to extend or duplicate the display. Kindly follow the below guide on how to do just that:
By default, you’ll likely see only one screen duplicates/extends the display. To change this, open Settings using the Windows + I hotkey.
You should see three screens numbered 1, 2, and 3. Select the display indicating no signal.
All the screens will likely flash for a second and start working as per the configuration.
If you wish to close the laptop and continue enjoying a dual monitor setup, here’s what you should do:
Next, set the ‘When I close the lid option’ to Do nothing for both On battery and Plugged in.
Finally, your laptop will turn on even if its lid is closed. This way, you can continue working on the two screens.
Citrix Workspace Not Launching: How to Fix it in 5 Steps You’ll be able to run Citrix Receiver after executing our solutions
If you can’t launch Citrix Receiver, your work-from-home day can be compromised
Your applications may cause the Citrix Receiver not to establish a problem.
You must ensure that you have full permission for the user account.
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Citrix Receiver for Windows 10 has incredible features that help you work smarter from anywhere. However, this doesn’t mean that you’ll never get to experience its fair share of flaws. For example, you could be out of luck when you can’t launch Citrix Receiver.
While you can do so without elevated permissions and logging in with the regular domain user account, you cannot launch any applications and desktops.
However, if you launch Citrix with elevated permissions and then log in with the regular domain user account, launching them works like a breeze. For those looking to fix this, that means one thing.Why is Citrix Receiver not working?
In addition, many problems were reported by our users, which were quite similar to one another. The following are examples of some of them:Is Citrix having issues today?
Today’s guide will show you how to quickly launch Citrix applications and desktops. So keep on reading the brief list of fixes you may apply in that regard.What can I do if the Citrix workspace is not launching? 1. Refresh applications
If one or more Citrix Receiver applications cannot be launched in Windows 10, refreshing them is arguably the first thing you should do.2. Ensure full permissions for the user account
If you’re looking to launch Citrix Receiver in Windows 10 with no effort again, note that Full Permissions are required for the user account. You will find this under HKEY_USERSUSER_SIDSOFTWARECitrix on the client machine.
We recommend ensuring you have full permissions for the user account used to log in to Citrix Receiver for the Registry Key.
If the domain user account does not have permissions, delete the Citrix Registry key after taking a backup. Then, exit Citrix Receiver and launch it back.
Expert tip:3. Use another remote working solution
If Citrix Receiver is only giving you more headaches, maybe you should try another remote desktop solution. This way, you may increase your productivity.
To be more exact, the recommended solution below can deliver applications and desktops to any employee from a network.
But the best part is that it’s not complicated to implement within any environment. Moreover, it addresses the most significant concern in a company network: data security.
You will be able to secure all the online communication channels. From data leakage and theft by using the Secure Sockets Layer (SSL), data encryption using FIPS, and 2-way identification.
All the data transfers are monitored and reported in a central console for the IT manager in a visual manner.
This solution is perfectly integrated with a lot of other security solutions. Some examples are SafeNet, DualShield, Azure MFA, RADIUS server, and Google Authenticator.
Start using this all-in-one remote work solution for accessing your applications.
Free Trial Visit website4. Download the latest version available 5. Reset Citrix Receiver
If things worsen and nothing seems to work, resetting the Citrix Receiver can help. Perform the above steps, and you should now be able to log onto Citrix as usual.
Note that resetting does not impact the Receiver or plug-in installation, per-machine ICA lockdown settings, or GPOs. However, accounts, apps delivered by the Self-Service plug-in, cached files, and saved passwords are removed.
Uninstalling with the receiver cleanup utility and reinstalling is also a potential solution. Just give it a go when all other fixes fail.
While each user has different expectations, this guide will provide a good overview of what you can do when you cannot launch Citrix Receiver in Windows 10/11.
For more potential solutions, check out how to fix the Citrix Receiver fatal error on Windows 10/11.
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Data engineering is that one field that has made it into the list of the most sought-after professions across the globe. This makes people curious as to how does the magical world of data science look like. On that note, one of the best ways to dive into the data engineering field is via a bootcamp. This is because bootcamps serve as an excellent medium to sharpen one’s skill-set. Once you attend a good bootcamp for data engineering, you’d be in a position to analyse different data types and deliver results as and when required. With that being said, have a look at the top data engineering bootcamps to attend in 2023.Data Science Dojo
Here, one can find online as well as in-person specialization. In-person training is held in select cities across the globe such as Atlanta, Austin, Bangalore, etc. However, considering the current situation of the pandemic, online classes alone are held. Though this bootcamp doesn’t promise the enrolled candidates of any employment assistance, but the certificate earned surely adds enormous value to your resume.Data engineering boot camp training (using Python and PySpark)
This is a 5 day data engineering bootcamp wherein the focus is laid on data engineering skills like disseminating and repairing data with Python and Spark SQL. By the end of the course, you’d be able to build production-ready distributed data infrastructure. Right from defining data engineering, NumPy, Pandas, data visualisation to Apache Spark and Apache Airflow, this course got you covered.Pipeline data engineering academy
This is a vast 12-week full-fledged data engineering bootcamp. One can either attend this online or opt for in-person assistance as per the convenience. Not one, not two but multiple areas such as logs, databases, 3rd party APIs, scraping, Google Analytics, ad-blocking, AWS, GCP, Azure, Python, and OS tools, such as Snowflake, SQS, Kinesis, PubSub, and Kafka are covered here. Well, there is more to this. The bootcamp students will learn principles of data warehousing and computation, such as dimension conformance, consolidation, indexing, distribution, partitioning, pre-computation, and query blueprints. All in all, this is worth every penny that you spend.Jigsaw Labs
Data engineering is that one field that has made it into the list of the most sought-after professions across the globe. This makes people curious as to how does the magical world of data science look like. On that note, one of the best ways to dive into the data engineering field is via a bootcamp. This is because bootcamps serve as an excellent medium to sharpen one’s skill-set. Once you attend a good bootcamp for data engineering, you’d be in a position to analyse different data types and deliver results as and when required. With that being said, have a look at the top data engineering bootcamps to attend in 2023.Here, one can find online as well as in-person specialization. In-person training is held in select cities across the globe such as Atlanta, Austin, Bangalore, etc. However, considering the current situation of the pandemic, online classes alone are held. Though this bootcamp doesn’t promise the enrolled candidates of any employment assistance, but the certificate earned surely adds enormous value to your chúng tôi is a 5 day data engineering bootcamp wherein the focus is laid on data engineering skills like disseminating and repairing data with Python and Spark SQL. By the end of the course, you’d be able to build production-ready distributed data infrastructure. Right from defining data engineering, NumPy, Pandas, data visualisation to Apache Spark and Apache Airflow, this course got you chúng tôi is a vast 12-week full-fledged data engineering bootcamp. One can either attend this online or opt for in-person assistance as per the convenience. Not one, not two but multiple areas such as logs, databases, 3rd party APIs, scraping, Google Analytics, ad-blocking, AWS, GCP, Azure, Python, and OS tools, such as Snowflake, SQS, Kinesis, PubSub, and Kafka are covered here. Well, there is more to this. The bootcamp students will learn principles of data warehousing and computation, such as dimension conformance, consolidation, indexing, distribution, partitioning, pre-computation, and query blueprints. All in all, this is worth every penny that you chúng tôi another extensive boot camp that is loaded with knowledge is that of Jigsaw Labs. Here, the bootcamp students can enrol themselves for either a 24-week Data engineering Bootcamp or a 12-week Web development Bootcamp with Python or both, depending on preference. The students would be enlightened about backend programming (Python, SQL, Flask and testing), how to work with professional codebases, DevOps (AWS, Docker), and data pipelines (Pyspark, Airflow, Redshift, DBT) in the data engineering bootcamp. Yet another strong reason as to why enrolling for data engineering bootcamp at Jigsaw Labs makes sense is because the students are guaranteed a job. Of course, there are a set of conditions that have to be met to grab a job at any of the esteemed organizations, but a point to note is that the conditions aren’t that difficult to accomplish. Well, when the end result is your dream job, who wouldn’t mind putting in every possible effort?
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