How can OCR help with RPA and Document Processing? — Techashton
OCR or Optical Character Recognition is a fun way to read and understand documents. But why does it even make sense? Let’s find out. But before we proceed, we need to wrap our head around a less common machine learning term: RPA (Robotic Process Automation).
For the unversed, RPA is Robotic Process Automation, which is instrumental in helping businesses eliminate repetitive tasks. Document processing happens to be one such task- where the focus is often on invoices and handwritten tasks. Simply put, OCR can help with this aspect of document processing- the one involving tons of error-prone identification.
So let’s dive right in and understand the process better:
What is OCR in RPA?
Trust us; we wouldn’t overcomplicate things. OCR or Optical Character Recognition is a machine learning tool capable of capturing printed text and handwritten notes. Once captured, OCR is also responsive for converting these unstructured data chunks into decipherable data that machines can read.
How OCR achieves all of that is a different question altogether? Here is a bird’s eye view of the same:
Working alongside different light patterns
Pre-processing images, if any, by de-skewing letters and even smoothing.
Detect discernable lines, which are characters and words
Producing machine-readable elements
Every OCR trait can be put to use by RPA bots, depending on the short- and long-term requirements.
What are the use cases of OCR in RPA?
Now that we have touched upon a more oversimplified version of OCR, let us take a closer look at the RPA relevant use-cases:
Human Resources, across organizations, can make good use of Optical Character Recognition for resume screening (for specific keywords that match job description), document management, expense management and reimbursement, and more.
As per estimates, sifting through massive new data chunks (close to 2.3 exabytes each year) is one of the top physician burnout reasons. And that is where OCR can help by assisting specific RPA initiatives, including patient registration, trial matching, and even creating EHR snippets for identifying patient progress.
Did you know that nearly 42% of financial processes can be automated? Paired with RPA initiatives, OCR can help achieve the same, especially when it comes to tracking invoices and relevant data, receipts, insurance documents, and even credit scoring.
Benefits of Intelligent OCR and RPA Integration
In a nutshell, every bit of data captured by OCR-driven RPA can be used as standalone assets or as usable machine learning information in the form of an OCR dataset or categorized training data.
Didn’t we just talk about the use-cases? We did, but there is still some scope to discuss the benefits. In brief, OCR integrated with RPA lets you digitize and decrypt any document. Here are the more targeted benefits of the approach:
An OCR-backed RPA application can make extracting data more accurate than ever. Robots meant to scan through and scrap data work more accurately with OCR functionality embedded within.
Well, this benefit requires no validation. Anything automated is always faster.
OCR integration makes RPA resources read emails, images, and even PDFs better.
And most importantly, OCR implementation helps you save a lot of effort and also money in the process.
Wrap-Up
Author Bio
The world is changing. And so are the processes defining it. As we move further and further away from grunt processes, document processing often needs to be the first roadblock to cross- due to the sheer size and volume of organizational data. At this point, shifting focus to RPA applications and tools powered by nifty artificial intelligence technologies like OCR is advisable. From robots scanning text to setting up a visual automation process, optical character recognition can be the game changer for several global verticals, as mentioned descriptively in the sections mentioned above.
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip.com, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
Macroeconomic issues such as supply chain disruption, input cost inflation, and employee shortages continue to dominate headlines. Amidst the uncertainty, organizations are turning to technology to solve their “heart of the business” challenges. Here at Master of Code, we continue to see a significant uptick in technology investment in automation and Conversational AI to streamline internal workflows and scale customer communications.
Conversational AI scope withing the company activities
A major theme we continue to see evolve is cross-team technology adoption. Conversational AI, with its battle-tested business case in a customer support setting, is spreading throughout the organization. For instance, organizations want an integrated approach to engaging with a customer across the customer journey through Marketing, Sales, Customer Success, and Customer Support teams. In the past, these teams typically made technology investments in isolation. As these silos break down, technology investments need to consider the unique needs of each team and how they are interrelated in an increasingly matrixed organization.
In uncertain times, planning often takes a backseat to urgency and action. Over the past year, we’ve helped our clients invest in Conversational AI innovation and increased 7.67x weekly bookings or conversion rate 3x higher since chatbot was launched, while avoiding several common mistakes during the planning and scoping phase.
Benefits of AI-chatbot implementation for business
Top mistakes during Conversational AI implementation planning
Validate Conversational AI Use Case First
In our experience, teams that experiment with low-risk projects focused on use case validation typically introduce more innovation over the long term. We also find that many organizations underutilize their existing technology investments. We encourage teams to use technology that already contains Conversational AI capabilities built into the platform. Typically, it’s much quicker to develop and validate a proof of concept than to evaluate and implement new technology. For instance, using common enterprise software, we can build a virtual agent in less than one sprint cycle using tools including but not limited to:
Digital Transformation in the Cloud (Google Cloud, Microsoft Azure, AWS)
Customer & Agent Support (LivePerson, Zendesk, Hubspot)
Measure Twice Cut Once
Urgency and excitement can lead teams to make sub-optimal technology decisions. Within the Conversational AI ecosystem, there are a significant number of platforms, software, and other vendors offering cutting-edge AI solutions. Changing technology platforms is a costly endeavor including the porting of Natural Language Understanding (NLU) models, existing chatbots and virtual agents, and building new integrations. A transition can set back a team 6–12 months of development time and spend. We work with clients to implement a Technology Evaluation Framework to ensure the best technology decisions are made. Choose the platform that addresses your unique use case, industry, organizational size, architecture, and administrative needs.
In the first quarter of 2022, Master of Code has helped a record number of clients navigate Conversational AI and Conversation Design solutions. What is your next opportunity to innovate?
Exploring technology platforms? Check out the Checklist available below.
In the healthcare industry, every fraction of time weighs significance. People do not want to engage in waiting lines or sit by the phone looking out for a response from medical professionals. In this post-pandemic world, healthcare providers have to be more keen-eyed with their approach to customer service. Given the sense of fear and watchfulness the virus has evoked among people, it is vital for the healthcare industry to stay ahead of the game. This takes them to the implementation of healthcare chatbots.
The global chatbot market was valued at US $526 million in 2021. The research estimates that it will be US $3,619 million by 2030, at a CAGR of 23.9% during the forecast period. This technology trend has more rewards for healthcare service providers than you know. If you’re planning to implement a chatbot to boost your operations, there’s a lot you’d expect it to offer. Since that totally depends on how you design it, we’ve brought you the top 10 benefits of chatbots in the US healthcare industry that indicate how healthcare chatbots should work.
10 Benefits of Healthcare Chatbots You Cannot Miss
The medical industry is as vast as it gets. From guidance on prescriptions to health emergencies, people reach out to healthcare providers for several reasons. While a call or email may be a straightforward mode for interaction, it is not necessarily effective.
Chatbots in the medical sector are booming owing to many such reasons. The healthcare chatbots market stood at around US $184.60 Million in 2021 and is forecast to reach US $431.47 Million by 2028. Let’s take a look at the benefits of chatbots in the medical industry that are adding to their whopping success.
Enables Prompt Response
People expect medical professionals to provide a quick response to their queries. Delays in responsiveness can lead them to lose trust in the brand they’re seeking assistance from.
Chatbots give a hand in taking care of customers’ queries and issues anytime. They make instant responses and resolve the case on a chat. It eliminates the need for writing an email or making customers wait.
The medical concern is not something that can wait for the resolution to come their way after hours. It is rather a time-sensitive stance. Medical brands employ chatbots to reply to the standard queries, which wrap up various conversations without human intervention and save time.
Boosts Customer Service
One of the most effective ways to engage and retain customers is to be there for them. When the staff is communicating with customers via chat or a phone call, they can only go up to a certain number in a day.
In the case of chatbots, things are the opposite. Chatbots are not restricted to a specific number of customers to handle in a day or at the same time. They are devised to address unlimited queries simultaneously.
In manual customer service, the requirements for more live agents increase with the spike in the number of customers. The implementation of a chatbot enables US healthcare providers to maintain their customer service without losing consistency.
Medical Assistance on Time
Medical emergencies happen all the time. Whether someone wants to know how to deal with a situation or how to proceed with a prescription, people immediately call the healthcare providers for assistance.
However, a number of people seeking help can exhaust the calling service. So, people can now opt to chat with a healthcare chatbot and get medical advice on what and how to move ahead with a circumstance.
Options like a menu of general queries, links to relevant solutions, etc., make chatbots a primary way to address an inquiry. Healthcare chatbots, if devised well, can work wonders. The best part about them is that they are available 24/7 to assist people.
Assistance with Medications and Reminders
Doctors typically guide their patients about the medications they’ve been prescribed and how they must consume them. They may prescribe different medications to help patients treat various health conditions.
The challenge is making sure that patients are taking the prescription seriously and following the course as recommended. According to a study, about half of patients don’t follow their medication course routinely or simply forget to do that.
Healthcare chatbots act as an amazing resort to make sure this gap is bridged. First of all, they help patients with medical advice. They also send daily reminders to them. Many chatbots in the US healthcare industry work as personal health trackers and medication reminders for patients that use them.
Scheduling
Scheduling is one of the topmost benefits of healthcare chatbots. Making a phone call may be a common way to schedule an appointment but it can be time-consuming for both parties. In this process, a patient calls their local health care provider and waits while the agent checks what slots are available. It can result in a good waiting time and higher costs.
Today, many medical professionals in the US are using healthcare chatbots that provide patients with an option to book appointments with the right doctor. Patients can easily use the scheduling bot from a website. They can also use it on their mobile device no matter where they are.
Some healthcare chatbots are even designed to send reminders and let people know when they have an appointment coming up. Moreover, these reminders can also communicate the specific actions they must take.
Reduced Costs
Healthcare chatbots are capable of managing a myriad of healthcare inquiries, including medication assistance and appointments. So, healthcare providers can be assured of a timely resolution to their patients’ queries. Moreover, people can access options like reminders, scheduling, and informational content.
The extensive range of concerns these services cover boils down to reduced costs. Since healthcare chatbots eliminate a pretty good slice of manual effort, it boils down to reduced costs. It is one of the well-enjoyed advantages of chatbots in the US healthcare industry or any industry for that matter.
Chatbots can help healthcare businesses save a good deal of money and contribute to other crisis investments the entrepreneurs might want to make. By implementing a chatbot, a healthcare service provider can eradicate the costs spent on hiring additional customer support agents and providing training.
Easy Time Receiving Feedback
Every task a healthcare provider performs, and every goal they set is an effort to provide the best services to their patients. This is one of the reasons why medical assistants are not shying away from implementing a chatbot to ease their job. One of the greatest reasons they are using healthcare chatbots is to have an easy collection of feedback. Healthcare providers can leverage the feedback they receive to make smarter decisions and improve their practices.
Healthcare providers send out customer surveys to collect feedback. However, only a few of them return with a response. Customers do not want to invest time in filling out a feedback form, or they are simply not interested. Businesses have started resorting to chatbots to measure customer satisfaction. Patients can chat with the bot, reply to the instant questions that pop up and rate their overall experience.
This method of collecting feedback works more efficiently, given that chatbots make communication faster and quite straightforward. Collecting feedback is a great way to boost relationships with customers as it shows that you value your patients’ opinions. With an automated pinch and instant response, making it possible just becomes easier.
Seamless Invoicing
Invoicing and tracking every payment can be a costly deal. Not to mention, the manual process demands more time and effort. Healthcare chatbots are making the process of medical billing easier than ever.
The chatbot enables healthcare providers to receive the amount due for the treatment they offer to their patients. The automation capabilities of a chatbot help healthcare providers create invoices and receive compensation for the service. Ultimately, it minimizes the expenses incurred by administration practices.
Increased Employee Productivity
Yes, chatbots do act as a productivity booster in the organization. Generally, a bot is employed to host customer queries and resolve them effectively. However, healthcare companies can also leverage them to support collaboration among employees.
There are times when your employees want to confirm something or learn how a specific service works. When such cases occur, they can navigate to the website of the company and ask the chatbot for assistance. If you choose to build a custom healthcare chatbot for your company, you can devise it to link to various forms of content, including blogs and training videos.
More Value to Business Growth
Considering the top 9 benefits of healthcare chatbots we read, it is easy to surmise the role a chatbot plays in the growth of a healthcare company. Keeping in touch with patients 24/7 is beneficial in the long run. Moreover, a chatbot also improves brand visibility.
There are a multitude of factors that affect your website’s presence on online platforms. The time users spend on your website is one of the most powerful ones out there. The time your patients spend interacting with your chatbot adds value to your page. So, utilizing chatbots is an incredible way to boost customer engagement on the website.
Final Thoughts
Chatbots are growing exponentially in every industry. Healthcare companies can introduce them to their pages and make sure their customers are getting the best service. From on-time medical help to a quick reminder to take meds, a bot can be your patients’ support. It is imperative to do your research and define your goals before you build a healthcare chatbot. Being mindful with the planning and setting expectations will pose a beneficial factor for implementing this software.
Reading, comprehending, and summarizing long documents won’t be that difficult from now because linguistic startup AI21 has unveiled a tool that will automatically summarize, comprehend and read any long document in just a few seconds. This new Wordtune Read will make managing important documents easier for any kind of commercial and enterprise use.
Word and Wordtune
The company is considering this Wordtune Read as an important tool to save a lot of time for enterprises as it automatically detects and analyzes texts from a link or a given pdf and then summarizes, highlights, and identifies important phrases. Also, there’s a new feature called Spotlight that will allow Wordtune Read to reexamine and summarize with a different emphasis.
This Wordtune read has been created by AI21 intending to make it a part of its larger developing language models.
“ Our mission at AI21 Labs is to fundamentally reimagine the way people write and read, so we thought it was high time to share our vision for the reading part of that equation. Underpinned by our sophisticated language models, Wordtune Read was specifically designed to help professionals across a range of industries and academia navigate the issue of information overload,” AI21 stated in a Blog Post.
In every business, it is important to understand your customers but no more so than in the world of finance — where the stakes are comparatively much higher. For a bank, each customer presents a risk, especially when it comes to loaning money. Bad loans are full of risk, good loans are not. Ultimately, deciding whether that risk is worth taking is a decision of paramount importance and can affect the bank balances of people the world over.
With this kind of responsibility at stake, banks tend to analyse their customer’s finances with the utmost scrutiny. Loans are granted — only — when an applicant meets certain criteria. But for people outside of the financial sector, these characteristics often seem mysterious, tangled and daunting.
We were interested in finding the characteristics of loans that were most important to banks and lenders.
Aside from a person’s credit history, there is a multitude of factors that help banks make decisions on the suitability of a loan. Factors including whether or not someone is a homeowner, a person’s age, their direct debit history, and how much they earn all contribute to a risk profile.
But disclosure of these characteristics, how applicants are scored and which features make it more or less likely for loan applications to be approved, are not as transparent as many of us would like them to be.
Due to the privacy with which datasets of loan applications are protected, the challenge of finding a dataset to explore proved to be more difficult than usual. We had to change our angle … and our time period. Putting on our beaded necklaces and frayed jeans, the team began to look into a dataset of loan applications … from 1970.
Putting on our beaded necklaces and frayed jeans, the team began to look into a dataset of loan applications … from 1970.
Our team decided to build a project using the features of an application to predict whether an applicant would have a good or bad risk rating. This meant reverse-engineering the features of a loan application in order to understand how they related to the loan’s risk rating. With Graphext, you can do this using the visual editor in under 10 minutes.
Our model used Risk as it’s target variable and every other characteristic of a loan application as factors, clustering loan applications based on the similarity of these factors. Our intention was to uncover the constitution of high-risk or low-risk applicants.
A Successful Model
A model’s error tells us whether the model was able to recognise a relationship between the factors and the target variables.
After executing the model, the team started to inspect the Graph, the network visualisation wherein loan applications were grouped together into clusters. A models accuracy — or error score — is important. This helps us recognise whether the model was able to recognise a relationship between the factors and the target variables.
The model we built had a low error score — 57 incorrect risk predictions out of a dataset of 1000 loan applications. It seemed to be clear that the model was able to understand which factors were most significant in leading to a risk rating. Next, using the Graph, we set about analysing the data to identify the factors most strongly contributing towards a bad risk rating.
Value Distribution in the Dataset
Cluster 9: A Bad Risk Cluster
With Cluster 9 making up just 5.6% of the dataset but having double the average score for bad risk, we started to inspect this important cluster in order to analyse the make up of a bad risk applicant.
After filtering our project to select data inside cluster 9, we could explore the features of this cluster dynamically. Cluster 9 loan applications are for higher sums of money and for long payback periods with smaller instalment rates. Moreover, applicants are generally younger people between the ages of 20 and 36 looking to start a business.
Cluster 1: A Good Risk Cluster
In order to evaluate the reverse of this, we could see that Cluster 1 had the highest number of good risk applications. It made up 10% of the dataset and in this, 79% of applications were classified as being good risk — 10% higher than everything in the dataset.
This cluster was predominantly made up of people in their thirties who were single. I found the fact that single was a positive factor for good risk very interesting, as I would consider that two incomes would be better than one, however this may be influenced by a lower likelihood of a married couple having two incomes in the 1970s.
The features of cluster 1 confirmed a few suspicions I was beginning to have about the dataset. Loan applications here were for short durations and low amounts. Additionally, the purpose of the loan was to buy either a TV or radio. Can you imagine taking a loan to buy a radio these days?
Social vs Financial Factors
The characteristics of a loan application can be seen as either financial or social factors. Age and status are variables relating to an applicant’s social status, whereas savings account and loan duration are variables relating to their financial status.
Social Factors
Selecting bad risk loan applications and inspecting their features suggested that women were more likely to receive bad risk ratings. The same is true for younger people, especially loan applicants under the age of 26.
It was also interesting to note that homeownership helped to increase the positivity of an applicant’s risk profile. Tenants or those in free housing were more likely to receive a bad risk rating.
With women and younger people coming out worse, I sincerely hope brokers and banks have changed their attitude changed since 1970. It would make for an interesting comparative study to find out just how much of this bias still applies.
Financial Factors
I am less persuaded that the influence of financial factors will have changed over the past half a century. Longer loans for higher sums of money are profiled as bad risk — and this makes sense! Not only this, but applicants with more savings and longer periods of current employment are more likely to be awarded good risk profiles.
So, with all this in mind, the ideal candidate to receive a bank loan in 1970s Germany would be a single man who already owns their own property and who is over 32 years old. He would have been in his current job for a sustained period and would have a reasonable amount of savings. Furthermore, he would be looking for a low sum of money over a short payback period.
At Master of Code, we talk a lot about Conversation Design, how to plan a conversation flow diagram and the importance of the flow and feel of a chatbot. Customer support, user or patient management, and answering customer questions are all great use cases that a conversational AI chatbot is perfect for. Another great chatbot application is one that can help frustrated customers solve issues and give back some time to agents: troubleshooting use cases.
In a troubleshooting flow of a conversational solution, a bot’s main goal is to automate simple fixes to free up agents’ time for more complicated issues. The customer’s goal is to get their issue fixed without waiting for help.
While it’s nice for a chatbot to be empathetic and conversational, it becomes more important to get each point or troubleshooting flow step across in the least amount of words (and the lightest cognitive load possible).
Here’s how you can make your troubleshooting chatbot conversation flows more impactful
Edit, edit, edit
For your chatbot, you can borrow from the world of UX, copy editing, and heuristics to drive troubleshooting conversation flow strategy and find clarity in your messaging. Taper messages down as much as possible by:
Removing unnecessary words that clutter the chatbot messages.
Removing redundant language that dilutes the meaning.
Using imagery for guidance.
Editing down your bot’s messages within troubleshooting conversation flows
By simplifying messages, you’re already one step closer to helping customers achieve their goal by alleviating some of the cognitive load for them.
Another way to make chatbot troubleshooting content more digestible is to break longer messages into two messages. Keep in mind that it takes two to converse and a bot should never hog the conversation or overwhelm the customer with too much information at once.
Review your conversational flow diagram and be cognizant of how many messages your bot is consecutively sending. To ensure you’re not sending more than three messages in a row, invite the customer to let you know where they’re at in the process by interacting with you. You can use quick replies or give options that:
lead to other relevant information in the conversation flow or in another flow.
let customers connect to a live agent if they’ve run into an issue that the bot can’t help with.
use Natural Language Processing (NLP) to let customers stop troubleshooting whenever they want to.
give the customer the option to let the bot know when they’re done and ready to move on.
Keep in mind that not every point in a troubleshooting chatbot conversation flows should be an escalation point. In other words, there’s no need to add the option to connect with a live agent after one or two standard troubleshooting steps to call center automation. Doing so could lower your containment rate and increase wait times. Offering escalation at a key step in the beginning and at the end of the conversation flow is plenty.
Consider other paths of conversational flow
Another important piece of troubleshooting with a chatbot is to remember that customers may finish the troubleshooting conversation flow and move on to another flow in the bot. For example, a customer may need to activate a product or service before they set it up. This means that they could start in an activation troubleshooting conversation flow and end up in a set up flow.
To understand your customer’s journey, review bot conversations and analytics to see where customers are going and identify patterns into subsequent flows. This will help you anticipate where your customer needs to go next and how to map customer journey. Keep flows smooth and consistent by paying attention to how much content the customer will see when they transition flows.
The takeaway of troubleshooting chatbot conversation flows
Technical jargon and language in a chatbot is inherently more difficult to understand than checking out or asking an FAQ. On top of that, customers are often frustrated when one of your products or services isn’t working for them. To make their lives easier, always keep your troubleshooting messaging short, to-the-point, and cooperative.
Well-designed troubleshooting conversation flows require a good amount of upfront work and continued data-based optimization by Conversation Design specialists. Following the tips above will help you build a useful, frictionless and efficient experience that’s a win-win for you and for your customers.
Ready to build efficient troubleshooting conversation flows as part of your impactful conversational AI solution? Let’s chat! Get in touch with us!
Can you answer one simple question? What does it take to stay alert on road? Coffee, tea, music, conversations, and window down for some people. But, is it the safest solution?
Then what’s the solution? With the recent advancement in AI technology, there is one technology solution is at your rescue and that is the Driver Monitoring System.
Let’s go through this blog and learn about Driver Monitoring System from scratch and how it is a life savior jacket for drivers.
The driver monitoring system is an advanced safety feature that utilizes a camera mounting on the dashboard to monitor the driver’s alertness and drowsiness. In case the driver is getting sleepy and distracted driver monitoring system generates an alert and recommends taking a break.
This advanced safety feature is gaining huge attraction from car manufacturers, and as per a report the global driver assistance system growth is expected to reach $32 billion by 2025. And why not, who doesn’t want more safety while driving on highways and traffic.
Driver monitoring system typically uses a driver-facing camera equipped with infrared-light-emitting diodes or laser light that can see the driver’s face even in dark. Using these diodes, the driver monitoring system can also check whether the driver is wearing sunglasses or not.
Isn’t it amazing? This advanced driver monitoring uses training datasets points to check the driver’s action and generate points. Let’s understand how the driver monitoring system works.
This safety system monitors the driver’s most recent behavior right from the head position, and eye, and further gives a signal from cars such as vehicle speed and traffic signs. According to the data accessed by the driver monitoring system, driver alertness is graded from 1 (attentive) to 4 (distracted). That’s how this information can be used to dramatically adapt driver assistance functions such as emergency car brake assist and blind-spot detection warnings.
Also, the driver monitoring system diagnoses the driver’s drowsiness level based on blink duration and frequency as well as eye-opening and closing frequency. Based on these points, the driver monitoring system calculates the drowsiness scale from 1 (alert) to 4 (sleepy). If the driver is sleepy it generates an alert immediately.
The driver monitoring system is a boon in disguise to maintain driver safety and lower the count of accidents that occurs due to drowsiness and non-focus during driving. Some of the driver monitoring system benefits include:
Increase driver safety by generating alerts for drowsiness and alertness detection
Enhance comfort by offering automated parking and easy traffic sign recognition feature
Give a boost to autonomous driving L2+ and scalable solution for small and niche application
Help in avoiding collision and fatal accidents
The first and most important step in building a driver monitoring system is a massive amount of training data. This training data helps the deep neural algorithm to perform multiple automotive AI process such as face identification, voice and image recognition, and object identification, by training this interference dataset give outcomes.
To identify objects, the driver monitoring system has to be trained rigorously on thousands of images that made it easier for them to identify the objects and the content in the image. And to make an autonomous driving monitoring system requires a large amount of labeled and unlabeled trained datasets that can navigate through complex driving scenarios at a faster rate.
Adapting to the next frontier technology is a must-have in the digital era when safety is the biggest concern. From maintaining driver’s focus to generating alerts to drivers in the case of emergency, requires the safest solution that can be easily accessible. And drive monitoring system is a solution that allows drives to maintain their focus on road and the rest will be handled by the system itself.
Its high time to adopt the design and innovation in drive monitoring system technology and offer drivers a more safe, secure, and comfortable ride without any fear of accident and casualties.
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Have you ever wondered about the amount of data that an organization holds in the form of papers and multiple formats in its system all across the organization? The answer is innumerable. Data is the key to business growth and its keeps accelerating with time. For example, you have a pdf life and you can’t make any changes until it’s in editable format. To copy the text directly requires you to convert the file into text format and then process it. Considering the time and effort involved in the whole process makes the place for technology adoption to fast-track the data processing. And the answer lies in Optical Character Recognition (OCR). Do you think OCR can add value to your business and organization, let’s dive into the basics of Optical Character Recognition (OCR) and its most relevant use cases all across industries?
What is Optical Character Recognition (OCR)?
When we talk about Optical Character Recognition (OCR), it is a field of Artificial Intelligence (AI) that is specifically related to computer vision and pattern recognition. OCR refers to the process of extracting information from multiple data formats like images, pdf, handwritten notes, and scanned documents and converting them into digital format for further processing.
Using OCR, organizations can easily encode the text from an image and allow it to be electronically formatted, searched, and compacted on a digital system or web for use.
Optical Character Recognition (OCR) Use Cases across Industries
Banking and Insurance
Think about the data banking and financial organization carry from transactions, KYC applications, contract orders, reconciliation, and loan applications to customer services. Leveraging OCR technology offers the banking industry to streamline and digitize the whole process for end-to-end document processing and easy data access to employees. For example- with advanced machine learning algorithms OCR technology offer banks to recognize their bank card with accuracy even if the image data quality is low.
Traveling
OCR technologies also make traveling an easy path for you by fast-tracking the passport checking and travel application for security and data storage purposes. Also, OCR helps in reducing manual errors in data verification and processing the data faster than humans. From booking the checking process to travel expense management, OCR technology can be leveraged for reimagining the travel industry and customer experience as well.
Government
Many government and legal industries still rely on the paper trail method to process information. From license registration to voting ID cards, OCR technology can be used to streamline the workflow. Instead of manual verification, OCR technology can easily check the registered users’ information just by scanning the card and accessing information in a smooth process way.
Healthcare
The healthcare industry can get the most out of OCR technology. Using OCR features healthcare organizations can get a medical history, past illness records, and prescriptions just with one click. Also, medical providers can encode this information and access it in the future anytime using the OCR feature.
Food Industry
Using machine learning algorithms with OCR features can help the food industry in creating a digitized menu, and a database of multiple recipes with calorie information and their apt intake amount. That’s how OCR feature not only give enhance the customer experience but also give the food industry an idea for creating more innovative process.
Logistics
The logistics industry spends most of the time on routine input and manual data process of logistics documents, training modules for engineers, design, and development of new machines. Leveraging the OCR feature can streamline the process by checking the in [put data autonomously and removing the chance of error while loading.
Retail
OCR feature can up-level the retail operations by offering retail industries to scan and extract the relevant information from bills of payment, packing lists, invoices, purchase order’s, and many other processes. In addition, OCR can create structured format data for easy data access and offer end-to-end data processing.
Time to act now
In the digital era, the most critical factor for any business and organization is the customer satisfaction and data accessibility. And Optical Character Recognition feature has all on its plate to serve you. From extracting data faster to making the user’s lives easier, OCR has a lot in store for you. All it requires is adaptation. Build a streamlined business process with OCE before it’s too late.
Author Bio
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Linkedin: https://www.linkedin.com/in/vatsal-ghiya-4191855/, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
What is synthetic Data in machine learning, and why do you need it? — Do It Easy With ScienceProg
As the name suggests, synthetic data is the data that is artificially generated rather than being created by actual events. In marketing, social media, healthcare, finance, and security, synthetic data helps build more innovative solutions.
Data is the key to resolution and quality service, whether you are processing an invoice or extracting information from a centralized legacy system.
Many organizations complain that collecting and using data raises privacy concerns and leave their business to data breaching issues. Also, some data is tough to collect and incurs a high cost to the organization. For example, collecting data related to real-time events like banking transactions and road events for autonomous vehicles take a heavy load on organization costing.
Then, what’s the solution to collect and use data without breaches, fines, or punishment. The answer is in Synthetic Data. Do you want to know what synthetic data is, why you need it, and how you can make the best use of synthetic data? Let’s get rolled into this blog and learn all about Synthetic data.
What is Synthetic Data?
Synthetic data is annotated information that computer algorithms generate as alternative data. If we put it in a simple form, Synthetic data is created digitally rather than collected or measured manually in the real world.
The significant advancement in the rise of synthetic data is a more data-centric approach to Artificial Intelligence technologies like Machine Learning. As per Gartner’s prediction, “By 2030, most of the data used in AI will be artificially generated by rules statistical methods, simulation or other techniques.”
Synthetic Data vs. Real Data
When it comes to using real data, it’s tricky, as real data contain the information that researchers don’t want to disclose as it might lead to data breaches and privacy violations.
But, with synthetic data, privacy is not a concern. Synthetic data ensures-
Labeled data in a uniform way
Privacy and confidentiality
Error-free data
Data collation from multiple resources
Existing Data scalability
Data quality and balance
Why is Synthetic data important?
Most developers need data to create neural networks for machine learning as diverse training datasets create more powerful AI models. But extraction and labeling data contain a few thousand to ten million elements that are time-consuming and expensive. But synthetic data reduce the burden of collecting and labeling millions of data and the cost related to it. Synthetic data ensures that you have the label data with all the diversity to represent in the real world.
Benefits of Synthetic Data
Synthetic data comes with a range of benefits for almost every industry type, from finance to healthcare. The best way it helps an organization is to reduce the need to capture images and data from the real world and make it possible to generate and construct a dataset much more quickly than a dataset that depends on real-time events. Synthetic data has a variety of use cases and can be applied to any machine learning task or process. Some of the typical applications of synthetic data are-
Using synthetic data helps vehicle manufacturers create training data for cars at a real-time pace to avoid accidents and casualties. Also, the banking and finance industry can benefit from synthetic data by creating a fraud detection model for detecting fraudulent transactions and maintaining data security.
In addition, healthcare organizations can leverage synthetic datasets to train AI models for better medical imaging and patient care while protecting patient privacy.
Challenges of Synthetic Data
However, synthetic data comes with a range of benefits but specific challenges that make a tough call for organizations. These challenges include:
As synthetic data is not the replica of actual data, it might not cover the original data’s outliners.
Synthetic data might reflect biases in data as the quality of synthetic data is dependent on the quality of input data and data generation model.
Synthetic data adoption might be slower due to no witness of benefits before.
It takes huge time and effort to generate synthetic data.
Synthetic Data- Key to unlocking new Possibilities
Compared to real-time data synthetic dataset generation model is much faster, accurate, and time savvy. From allowing data science engineers to train machine learning models to create an autonomous AI, synthetic data has a lot on its plate to drive in this data-centric era.
It’s not only a replacement for real-world data but is much bigger than that. Synthetic data offer data scientists to create innovative solutions that are almost impossible with real data alone. It’s time to pedal for a better world with synthetic data before it’s too late; act Now!
Author Bio
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
The ability infused into the machines- making them capable of interacting in the most humane ways possible- has a different kind of high to it. Yet, the question remains, how does conversational AI work in real-time and what kind of technology is powering its very existence.
Let’s find out:
Simply put, Conversational AI is a segment or sub-domain of Artificial Intelligence — aimed at enabling humans to interact more seamlessly with computing entities.
Remember those quirky website chatboxes that return specific answers to even some of the trickiest questions! Well, those are exactly the Conversational AI products we are talking about. Yet, the question remains: how come these chatbots become so perceptive. Surely, it isn’t just about feeding raw training data, like human voice and text, into the relevant models. There has to be more.
Often touted as a computer science vertical, NLP or natural language processing focuses on helping computers understand voice and text better- allowing them to better interact with the world or rather us, the humans.
The power of NLP allows humans to communicate with intelligent systems using specific languages, like English. Also, NLP helps a computer get better at machine learning, which is a pathway for computing entities to develop ‘artificial’ intelligence.
NLP-based intelligence is fed using NLP engines, which is the core prepping component- responsible for interpreting speech and text- and eventually feeding the structured inputs right into the system. But there is a lot more to an NLP engine.
What can Natural Language Engines do?
As mentioned, NLP engines are important. Why, you ask? Keep reading on:
Vocab enhancement: Every conversational approach must keep the training stream open for new words. NLP engines can help with that- assisting with word, phrase, synonym, and descriptor additions.
Context determination: Every conversation is made of multiple stages. At one stage, the customer might just throw a random question, whereas, at any other stage, it might be a specific query to close the deal. A natural language processing engine helps the computer determine the context associated with every stage.
Entity identification: Imagine a customer typing the following command: ‘How can I procure the mentioned offer that is to start on 25th July 2022’? As you can see, the statement comprises a date, numbers, and even a description. Powerful NLP engines help machines identify these disparate entities to perfection.
Utterance detection: A question like ‘When can I expect the email?’ can be asked as ‘Where is my email?’ NLP engines help intelligent machines get accustomed to every variation of specific questions for added relevance by factoring in the nature of utterance- both verbal and textual.
Intent: ‘I would like my coupon code now’ looks a tad different than ‘Where the heck is my coupon code?’ While the first is a standard statement showing desire, the next might be borne out of frustration. Top-of-the-line machines are great at differentiating based on intent, thanks to the efficient natural language processing engines.
Yet, analyzing the intent requires us to dig deeper.
How does NLP help to analyze the Intent?
Before we delve deeper, it is important to understand that conversational intent can be Casual (including every emotion) or Business. And that is exactly when ‘NLP-powered’ Intent Analysis is looked at as an important tool used by NLP engines.
In simple words, intent analysis helps machines assess the exact or near-exact intention of any user input- often by extracting the relevant entities. And that is how a chatbot actually learns to pick up a suggestion, news, or even a complaint.
As far as the approach is concerned, NLP tools (precisely the engines) help parse multiple intents- feeding them into the machine as high-quality training data. A structured dataset, therefore, helps with intent analysis. On top of that, the structured dataset is further fragmented to include some tricky words.
Output-wise, the NLP engine analyzes the sentence and tries to analyze intent based on the data fed, plurality, positioning (position of words in a sentence), and conjugation. For speech, other factors are also taken into consideration. And that’s more or less how intent analysis takes place in the background.
According to detailed research conducted by Market & Markets, the NLP market is expected to be valued at $26.4 by the year 2024. That is a CAGR of 21%.
And their role in enhancing the quality of conversational AI isn’t limited to emotion, utterance, entity, or even intent analysis. NLP is also relevant to aspect mining, topic modeling, text summarization, and more- helping chatbots software, self-driving cars, home automation setups, and digital assistants get more intelligent over time. With NLP, the possibilities are virtually endless.
Author Bio
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
