Why Machine Learning Enhanced OCR is the Way Forward for Data Entry and Data Capture
When transitioning from manual data entry, or legacy Optical Character Recognition (OCR), to machine learning-enhanced OCR, a company can expect significant changes in quality, turnaround time, and cost.
For many years the rule of thumb when outsourcing manual data entry was “double-key-and-compare.” This was the best-in-class method to ensure accuracy when capturing data to minimize errors. This method involved two keyers keying the same data and hoping for a match. When a match would occur, the data was believed to be accurate or have a high enough chance of accuracy for it to become the final output. When a mismatch occurs, one or both keyers would have keyed the data incorrectly. When this happens, a third party, usually a supervisor, would have to review the data for quality control. This supervisor would make the determination as to which of the keyers was accurate. If both keying outputs were incorrect, the supervisor would make the correction.
The double-key-and-compare model was, at the time, the most accurate way to ensure data output quality remained high. Accuracy is the most crucial factor for most companies when outsourcing this type of work; using this method, accuracy rates could be as high as 95%. The problems with this method are the costs involved, the time it takes to turn around DE work, the continued possibility of human error, and scalability.
Employing multiple keying resources that potentially key the same data three separate times can require a lot of bodies which translates to higher costs. At the time, accurately keying in-house vs. outsourcing utilized the same or similar processes, so outsourcing did have some real cost benefits. This is no longer the case, as we’ll review later in this post.
The job of data entry, even in offshore locations, tends to be low-pay and low-skill, which is a recipe for high turnover rates that increase costs even more with the need for onboarding and training new employees. Every employee that leaves takes knowledge of the process with them, and every new employee that joins brings little to no knowledge of the process. This increases the potential for human error and can result in turnaround issues.
The turnaround in manual processes was always based on an outsourcer’s ability to provide not only the infrastructure to accommodate the number of employees needed to meet turnaround demands but the ability of the outsourcer to onboard and retain employees across multiple shifts to ensure SLA demands can be reached. For these same reasons scaling for new work would take time and have difficulty meeting clients needs.
The need for a better, faster, and cheaper solution drove many organizations, clients, and outsourcers, on and offshore, to move to automation in the form of Optical Character Recognition (OCR).
In large part, OCR is still very standard across many organizations and outsourcing companies. The biggest reason those who moved to OCR still use it is that the cost of implementation is very high. Unfortunately, the ROI that occurs for organizations using OCR isn’t as high as the promise of implementing the automation was promised.
Only organizations and outsourcers with very structured forms see great returns on OCR, and even they have to ensure a high level of manual quality control to ensure the output meets their needs.
OCR relies on a process of mapping forms. You show it where the data it captures from a form is supposed to be (exactly where it is supposed to be), and OCR will extract the characters in that area. Rules are usually applied to these areas, such as “Alpha or Numeric only,” “Number of characters,” “Date formats,” etc. These are always “go/no-go” rules and will reject anything that doesn’t conform to what is expected in the mapped fields.
It will also have difficulty with certain fonts and handwriting. When confronted with them, OCR doesn’t have a level of context that will allow it to discern between an “O” and a “0” or an “S” or a “5”. This means that the level of quality checks for any semi-structured, unstructured, or handwritten form must be at 100%. Which often puts costs at the same level as performing data entry using manual processes.
The requirement to have high-quality output through OCR almost always means always ensuring these manual QC checks, which impact costs and turnaround times. The introduction of manual checks can also introduce human error. Most OCRs only live up to the promises they were supposed to if the forms they are applied to are highly structured and use consistent machine print.
Now there is an automation alternative many companies are moving toward, Machine Learning (ML) enhanced OCR. The impact of ML on data capture and data entry is huge. It decreases costs over manual and OCR processes by as much as 40% while increasing quality to near perfect and decreasing turnaround times to minutes.
Here’s what you can generally expect when moving to ML-enhanced OCR:
- Accuracy: Machine learning-enhanced OCR can significantly improve accuracy compared to manual data entry. It reduces the likelihood of human errors and can achieve high recognition rates for printed and handwritten text.
- Error reduction: ML models can learn from large amounts of data and improve over time, minimizing OCR errors as the system becomes more refined.
- Enhanced capabilities: ML models can handle a wide range of languages, fonts, and document formats, making them versatile for various data entry tasks.
- Turnaround Time:
- Faster processing: Machine learning-enhanced OCR can process documents much faster than manual data entry, especially for large volumes of data. It can extract information from multiple documents simultaneously, reducing overall turnaround time.
- Real-time processing: Depending on the implementation, ML-enhanced OCR can provide real-time processing capabilities, enabling immediate data extraction and integration into downstream systems.
- Upfront investment: Implementing machine learning-enhanced OCR in-house can be pricey, but outsourcing this work to a managed service can significantly decrease the cost of data capture or data entry. Outsourcing usually involves no up-front costs and is usually transactional.
- Long-term savings: Transitioning to an outsourced ML-enhanced OCR leads to immediate and long-term cost savings. It eliminates or reduces the need for manual labor, minimizing the associated expenses such as salaries, training, and potential errors that require rework.
- Scalability: Machine learning models can handle large volumes of data without a proportional increase in costs, making it cost-effective for processing high volumes of documents.
A thorough evaluation and piloting phase can provide more accurate estimates for your use case.
iTech provides machine learning-enhanced OCR services for many organizations across multiple industries. If you are considering moving to a solution that provides better quality and faster turnarounds at costs Manual data capture or OCR data capture cannot provide, please reach out to us from the contact page, emailing firstname.lastname@example.org, or by calling (972) 456-9479.