When it comes to data capture, OCR or Optical Character Recognition companies make huge promises. While there is much truth to OCR’s claimed capabilities, it remains largely a work in progress. Still, the fact remains that OCR is a form of unsophisticated automation: it is just a robot that converts characters into data. It does not work well with handwriting, and it does not capture what you need if the data is not structured. This lack of structure means to ensure quality; you must always pair OCR with people.
When it gets combined with people, it causes even more challenges. Any speed gained via OCR is insignificant because a person must still repeat the process.
In addition, there is the cost of OCR license and support. Any good OCR will set you back a lot of money. So much so that the support employees wipe out any cost savings you would anticipate you will need to maintain quality.
What is OCR Data Capture?
OCR Data Capture turns scanned images of typed or handwritten documents into searchable electronic text. OCR Data Capture, reduces the need for human data entry.
This adaptable solution may be installed on any desktop or as a client-server program, and you can use it throughout a department or a whole company. Its intelligent nature enables it to accommodate a wide range of document processing scenarios and support various form types, resulting in a system that can handle a wide range of workflows and regulatory requirements.
There is no such thing as 100% accurate OCR software. The quantity of errors varies on the document’s quality, and kind, as well as the typeface, was chosen. These are examples of OCR errors, misreading letters, skipping over illegible letters, and mixing text from adjacent columns or image captions.
Even if the source document’s scanned image is high-grade quality, You must take further steps to clean up the OCR text. Correcting the inaccuracies caused by OCR takes a lot of time. You must manually compare the original paper and the electronic text. When typing text from a page, people make mistakes, but it is sometimes easier to skip the OCR phase.
OCR works best with typed materials of good quality. OCR software cannot easily read handwritten documents. Similarly, non-Latin typefaces and typed fonts that mimic handwriting cause many errors during the OCR process. OCR may not perform properly if the document has weak contrast, is crumpled or unclean, or the text and backdrop are comparable in darkness.
Why OCR Fails
While a traditional OCR system is a mainstay platform, it may appear to be the end-all, be-all answer for data capture. However, when data gets misunderstood or not read at all, it can be very frustrating.
So, why does OCR fail?
- Many OCR engines are unable to support and comprehend the complexities of a document’s input data.
- Traditional OCR technologies cannot process documents in a variety of formats.
- Certain OCR technologies only recognize the font of the first alphabet in a line and continue reading using that font, ignoring changing font sizes in the same line.
- Many OCR solutions cannot read either border or borderless tables, increasing the possibility of unexpected errors.
- Noises such as black gaps and garbage values are not removed by traditional OCR engines, resulting in an ambiguous output.
With all of these issues in place, there is a good chance that OCR accuracy and output quality will suffer. As a result, the traditional OCR platform is unpredictable, and decision-makers have significant difficulty due to the lack of precision.
Addressing OCR Issues
So, what might be a better method to incorporate automation and achieve the holy grail of quality, speed, and cost reduction? For this matter, there are two ways:
- Use a Machine Learning enhanced OCR to add intelligence to your automation; or
- Use an outsourced vendor to provide Software-as-a-Service.
Using ML-Enhanced OCR
The global economic system is thrust into new digital domains at breakneck speed with traditional OCR in place. A significant amount of data still gets produced and stored in paper-based, analog formats. So, businesses require solutions that make it simple to convert printed text and scanned paper documents into digital files that can capture human intelligence. This need is where Machine Learning-Enhanced Optical Character Recognition (OCR) comes in to save the day.
Yet, OCR’s tight restrictions and inability to adjust to its errors heavily rely on manual operations. Although, it is still a positive step toward full automation.
Optical Character Recognition (OCR) technology gets used to extract text from a document or image. Significant advancements occurred when OCR got established, and data no longer had to be rewritten manually. However, traditional OCR technology had substantial drawbacks, such as the following:
- Semi-structured and unstructured forms are not suitable for OCR. OCR text only works with printed text, not amorphous formats like handwritten text. The computer must first learn to read handwriting to perform optimally.
- OCR can follow simple rules, but it cannot make intelligent decisions when problems do arise. Scaling is challenging for companies with hundreds or thousands of providers. Traditional OCR systems necessitate the creation of new templates for each vendor and each change. This inconsistency leads to inaccuracies since the system will not extract data correctly without the rules getting followed.
- Traditional OCR Procedures still involve humans significantly. Because all papers must be thoroughly evaluated and then manually rectified, traditional OCR processes still rely on humans and, as a result, human errors.
Machine Learning Enhanced OCR emerged to solve the drawbacks of traditional OCR systems. Traditional OCR gets improved by Machine Learning Enhanced OCR, which adds a layer of context and flexibility.
To accomplish thorough data gathering and error-free final output, traditional OCR typically requires human participation. These time-consuming activities get eliminated using Machine Learning, a type of Artificial Intelligence.
Machine learning is a kind of data analysis that uses artificial intelligence to create analytical models. Machine learning allows computers to gain access to hidden insights by employing algorithms that regularly evaluate and learn from data. On the other hand, these digital gems get discovered without programming applications that explicitly look for them.
For the following reasons, this technology has become a vital component of a variety of growing and established industries:
- Machine Learning is constantly improving its understanding of data context and how you should treat it. Machine learning can easily absorb and consume an infinite amount of data with rapid analysis and assessment.
- The more you use Machine Learning software, the fewer mistakes it makes and the more complex decisions it can make. OCR technology powered by machine learning can help maintain a smooth workflow by providing outstanding text recognition accuracy. Organizations can automate data entry, eliminate manual processing, and deal with various data sets in real-time, resulting in reduced workloads, faster processing, and more accurate data outputs.
- ML does not rely on manual processes once it has gotten trained on a function. Algorithms for machine learning have a penchant for operating quickly. Because of the speed with which it consumes data, machine learning can tap into emerging trends and deliver real-time statistics and forecasts.
- Machine Learning easily handles both structured and unstructured data. OCR Machine Learning can assist with a variety of data formats and languages as well.
- Handwriting can be converted into data using Machine Learning. The purpose of data collection for every organization is to turn raw data into information and information into actionable insights. Combining image recognition techniques with a chosen machine learning algorithm can result in a program that consistently reads handwritten numerals with around 95% accuracy. The rate could be much higher depending on the machine learning technique employed.
Machine Learning, unlike traditional OCR, accomplishes these tasks by being able to read and make logical decisions that place data in context.
Optical Character Recognition, as previously established, converts any handwritten, typed, or printed text into a machine encrypted text. A photo or digital copy of a document, a scanned image, or a physical document, among other things, could be the source.
A passport, credentials in the form of credit card statements, bank account details, business cards, invoice details, or any other certification can all get digitized with the help of an outsourced OCR service provider. It is one of the most extensively used methods for digitizing published texts. You can use it for machine-related processing such as perceptive computing, translation pricing, text-to-speech conversion, data mining, medical transcription, etc.
For the following reasons, this gets utilized widely for pattern recognition in artificial intelligence, healthcare, legal, banking, and financial domains:
- Outsourcing OCR Services reduces the time and effort required to create an alphanumeric copy of any manuscript, allowing the parent organization to concentrate on more important tasks.
- The professional employees of the outsourcing organization may convert any record or text into numerous forms such as PDF files, HTML documents, MS Word, Excel, and so on.
- In comparison to data entry services, which involve manual skills, the cost of outsourcing OCR services is substantially lower. As a result, the cost incurred by the company is not greatly affected.
- A similar document can be seen by numerous workers simultaneously, allowing for a more efficient labor force.
- All analyzed manuscripts or documents have the option of being password-protected, keeping them safe with the outsourced business, and limiting access to a few employees.
- Outsourcing OCR services is a progressive step in document management services, and it has quickly become a requirement for any company or industry to flourish.
At its most basic level, data entry is highly repetitive and rote. There are many different sorts of data entry, but they all have the same goal: to convert an existing document or piece of data into a more useful digital format.
The good news is that after implementing more efficient technologies, such as Machine Learning Enhanced OCR or outsourcing OCR as a Software as a Service (SaaS), life will be a lot easier for the specialists behind the scenes.
For more information on how your company can integrate Machine learning paired OCR automation tools to meet your data capture needs, contact us today!