Why Machine Learning Enhanced OCR will Eliminate Manual Data Capture, and Traditional OCR
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Why Machine Learning Enhanced OCR will Eliminate Manual Data Capture, and Traditional OCR

30Jun
Read Time: 6 minutes

With Manual Data Capture and Traditional OCR in place, the global economic system gets propelled into new digital realms at a remarkable speed. While huge data still gets produced and stored in paper-based, digital formats do exist. Plus, they continue to become more commonplace.

Therefore, organizations need solutions that make it easy to convert printed text and scanned paper documents into digital files, with the ability to encapsulate human intelligence, enter Machine Learning-Enhanced Optical Character Recognition (OCR).

Manual Data Entry

For quite some time, manual data entry has been in existence to make the data entry procedure easier. However, manual data entry is entirely dependent on humans and their decisions. The human element causes the following issues:

It is Vulnerable to Common Human Errors

Researchers uncovered the significant error rate of humans entering data in a 2009 experiment.

During the experiment, researchers provided 215 university teachers 30 datasheets, each comprising six different categories of data that they had to handle. Researchers discovered that the participants made an average of 10.23 errors when processing the data after the experiment.

Concurrently, automatic software-checked entries recorded an average error rate of 0.38. The human eye depends on visual clues to detect errors, and these indications might be difficult to spot.

A high data entry error rate could potentially waste a lot of the organization’s funds long-term. It is apparent that the problem affects both small and large businesses; when a corporation makes a clerical error, it risks overspending on contractors or getting into financial or legal issues.

Data entry errors have cost firms millions of dollars in some case studies. As a result, cutting corners on process automation can end up being quite costly in the long haul. When it comes to the organization’s strategic and long-term development, the approach makes no sense.

Scalability Necessitates the Addition of More Bodies

Manual data input is erroneous, but it also takes a significant amount of time to accomplish.

Professional manual data entry from paper to digital is estimated to take between 10,000 and 15,000 keystrokes per hour on average. The issue will be considerably more difficult for complicated messages that require some comprehension before being digested.

Companies will have to train people to perform manual data entry or engage professionals to complete the process. For the first case scenario, the procedure will take a long time to finish before the training finishes. Companies, on the other hand, will have to spend money on outsourcing the process.

However, even if firms enlist the help of professionals, the threat of losing focus and making mistakes is great whenever a huge amount of data needs to be handled.

Employee Turnover is an Issue

Manual data entry is tiresome and uninteresting, not to mention time-consuming. So, for companies looking for ways to demoralize their staff, manual data entry can do just that!

Professionals have the ability and capability to participate in far more productive activities than simply transferring data from paper to digital. Moreover, it does not make sense to burden professionals with such jobs when automated solutions are already existent.

Intensive Training and Retraining

Quality checks must become an inherent element of the human data entry process because a mistake in a corporate record costs a lot of money. Ideally, a company will need to hire more employees or outsource the process to do this. However, this option comes at an additional cost that many small businesses cannot afford.

Ultimately, all of these problems will cause a shift in focus away from essential or strategic responsibilities. Manual data entry is inefficient, incorrect, and expensive. Given that such processes do not add much to the table, it is tough to comprehend why they are still in use.

Traditional OCR

On the other hand, while a positive step toward automation, OCR is still significantly reliant on manual processes due to its strict rules and inability to adapt to its flaws.

Optical Character Recognition (OCR) is a technique for extracting text from a document or image. The invention of OCR prompted substantial advancements, and data no longer had to be rewritten manually. Most people believed that this solution eliminated the need for human data entry when gathering data from documents because the output is extremely accurate when dealing with documents with minimal variability.

However, traditional OCR technology still entailed major issues, such as the following:

Forms that are Semi-structured or Unstructured are Not Suitable for OCR

Only printed text, not unstructured forms like handwritten text, operates well with OCR text. The computer must learn to read handwriting first to function ideally.

OCR is Capable of Following Simple Rules, But it is Unable to Make Intelligent Decisions Outside of those Rules

Companies with hundreds or more vendors have difficulty scaling. With traditional OCR solutions, new templates need to get set up for each vendor and every alteration. This manual setup leads to inaccuracies because, without the rules, data will not get extracted correctly.

Moreover, implementing new templates takes a long time and costs a lot of money. Setting up a new template takes several hours while processing a single invoice takes three minutes. The setup of templates, on-premise implementation, maintenance, and invoice processing fees are all included in the operation costs.

Humans are Still Substantially Involved in Traditional OCR Procedures

Humans, and consequently human errors, are still prevalent in traditional OCR procedures because all papers must be carefully reviewed and then manually repaired.

Machine Learning Enhanced OCR

To address the shortcomings of traditional OCR technologies, Machine Learning Enhanced OCR became emergent.

Machine Learning Enhanced OCR improves on traditional OCR by adding a layer of context and flexibility.

Traditional OCR frequently necessitates human intervention to achieve comprehensive data collection and error-free final results. Machine Learning, a kind of Artificial Intelligence, eliminates these time-consuming operations.

Machine learning is a type of data analysis that automates the creation of analytical models. Machine learning allows computers to access hidden insights through using algorithms that consistently examine and learn from data. However, these digital gems get discovered without the need for programming programs that specifically hunt for them.

This technology has currently become a critical component of several emerging and established industries for the following reasons:

Machine Learning is Constantly Improving its Ability to Comprehend Data Context and How It Should be Treated

With rapid analysis and review, machine learning may readily absorb and consume an endless quantity of data.

This strategy aids in the review and modification of messages in light of previous consumer encounters and behavior. Once a model has gotten created using several data sources, it can locate relevant variables. This model eliminates the need for complex integrations by focusing solely on accurate and concise data streams.

The More ML is Used, The Less it Makes Mistakes, and The More Complicated Decisions it Can Make

With excellent text-recognition accuracy, OCR technology driven by ML can help to maintain workflow seamlessly. Organizations may automate data entry, remove manual processing, and handle various data sets in real-time, resulting in lower workloads, faster processing, and accurate data outputs.

Once Trained on a Function, ML does not Rely on Manual Processes

Machine learning algorithms have a proclivity for working quickly. Machine learning can tap into developing patterns and provide real-time data and forecasts because of the speed with which it consumes data.

ML can Handle Both Structured and Unstructured Data with Ease

OCR Machine Learning can also help with a wide range of data formats and languages. Most traditional OCR solutions require individual translators for each language that is processed when it comes to languages. ML’s translation capabilities, on the other hand, are all-in-one, allowing companies to translate languages in real-time effortlessly.

Machine Learning OCR, unlike traditional OCR, can “learn.” If ML is unable to interpret specific data sets, a human can step in to validate them. This advancement has the additional advantage of “teaching” ML how to deal with this process in the future if it comes across a similar situation. When it does, it just follows the instructions it was given and automatically executes the interpretation process.

ML can frequently imitate people so well that a human hardly spots the difference between man and machine. You can see this advancement in the employment of chatbots for online customer interactions. When used to capture data from handwritten manuscripts, which OCR struggles with, machine learning often becomes adept at interpreting to the point where it can also mimic it.

Machine Learning can Convert Handwriting into Data

Every organization’s data gathering goal is to turn raw data into information, and meaningful, actionable insights.

A program may be constructed to reliably read handwritten numbers with roughly 95% accuracy by combining image recognition techniques with a chosen machine learning algorithm. Based on the machine learning technique used, the rate could be considerably higher.

Unlike standard OCR, Machine Learning achieves this by reading and making logical judgments that place data in context.

The learning algorithm, usually in a supervised learning model, uses training data to improve accuracy. The algorithm receives a label or answers for each row in the dataset to figure out which data matches which handwritten digit.

Conclusion

Data entry is, at its most basic level, incredibly monotonous and rote. Of course, there are various types of data entry, but they all have the same goal: to convert an existing document or portion of data into a more usable digital medium.

Although people specialize in data entry for a firm or organization, it is practically available in practically every current job. However, asking everyone to engage in a data input process has several fundamental drawbacks, most of which are related to the accuracy and security of the information produced. The most common entry errors are transcription and transposition errors, which can be highly costly on their own.

Moreover, these mistakes make it increasingly challenging for correct data entry and data analyst professionals to evaluate the accuracy of incoming data.

Machine learning, on the other hand, has the potential to benefit every data entering procedure. The good news is that life will be easier for the experts behind the scenes after more Machine Learning Enhanced OCR deployment.

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