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How OCR Machine Learning will Eliminate Manual Data Capture?

Read Time: 5 minutes

Data drives the economy, so it is no great wonder that organizations have a big appetite for it. However, collected data is useless until it has been read, and not all data is easy to read or structured in a way that is easy to translate.

This is where an Optical Character Recognition (OCR) solution is often employed. OCR digitizes semi-structured and unstructured data into an easily readable and editable format. This way, organizations can quickly analyze their information output and convert it into actionable insights. OCR is often deployed for invoice processing, document search, translations, sales order processing, etc.

Because of the considerable amount of raw data collected by organizations keeping up with the digital pace can be difficult. When employing a traditional OCR solution, they find that not only does it have difficulty when handling large data sets, it also contributes to already considerable workloads in addition to being time-consuming and error-prone.

To overcome these limitations, Machine learning (ML) powered OCR has emerged to meet current digital landscape needs.

Fostering A New Reality With OCR Machine Learning

Traditional OCR often requires human intervention for complete data capture and to ensure final outputs are error-free.

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

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

By introducing OCR Machine Learning algorithms, organizations can automate data entry, eliminate manual processing, and handle multiple data sets seamlessly in real-time; this creates minimized workloads, reduced processing times, and accurate error-free data outputs.

OCR Machine Learning also aids in the processing of a myriad of data types and languages. For languages, most traditional OCR solutions require individual translators for each language processed. Whereas Machine Learning translation capabilities are all-in-one, allowing organizations to translate languages effortlessly in real-time.

An example of this is Google translate, which utilizes ML and provides a translation from a common language to any other common language within a matter of seconds.

How Machine Learning Enhanced OCR Eliminates Manual Data Entry & Traditional OCR

Unlike traditional OCR, Machine Learning OCR “Learns”

If ML cannot interpret specific data sets a human can intervene to perform the validation. This has the added benefit of “teaching” ML how to handle this process should it ever encounter a similar instance. When it does, it simply follows the directions it was taught and performs the interpretation process automatically. It also learns pattern recognition and context.

Often ML can mimic people so well that an actual human cannot identify the difference between man and machine. Chat-bots used for online customer interactions are good examples of this. When used to capture data from handwritten documents, OCR struggles with ML often becomes so good at translating that it can also imitate it when needed.

The data collection goal of all organizations is to translate raw data into information and information into actionable insights. Machine Learning, unlike traditional OCR, does this with its ability to not just read, but to make logical decisions that put data into context.

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:

  1. Machine Learning is Constantly Improving its Ability to Comprehend Data Context and How It Should be Treated
  2. The More ML is Used, The Less it Makes Mistakes, and The More Complicated Decisions it Can Make
  3. Once Trained on a Function, ML does not Rely on Manual Processes
  4. ML can Handle Both Structured and Unstructured Data with Ease
  5. Machine Learning can Convert Handwriting into Data

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

With rapid analysis and review, machine learning OCR Data Capture may readily absorb and consume an endless quantity of data across industries like Account Payable, Retail etc

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.

2. 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.

3. 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.

4. 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.

5. 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.


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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