What is an OCR software?
Optical character recognition entails the electronic conversion of handwritten, printed, or image-based texts or other physical forms of documentation.
It helps businesses to digitalize various documentation processes, hence enabling better searchability and security, among other purposes. OCR tools are critical for the modern-day business, but how exactly do you choose the best OCR Software. With so many options available on the market, and several technical aspects to consider, OCR decisions can be a tough nut to crack.
Buyers have to think about the Document Conversion range of OCRs and classification ability, among other factors. For the average buyer, all these technicalities can be downright confusing.
But you needn’t worry.
In this article, we’ll discuss how to choose the best OCR software for your project, so you can know exactly how to get what’s needed for your business.
Let’s get started.
Contents
1. Document Conversion Range
What does flexibility mean when you’re talking about OCR technology?
According to industry standards, there are about two primary font categories namely OCR-B and OCR-A. Flexibility means an OCR software should be able to work with these fonts, among others.
Additionally, it also refers to an OCR tool’s ability to work with a range of characters that differ diversely in structure or format.
For example, data to be digitized may be discontinuous, like Asian characters, or sometimes it’s dotted. In some cases, the information may be italicized as well, among other styling choices.
Consequently, an excellent OCR software should have a depth of conversion range in terms of format or character nature.
But there’s another conversion element to think about: language.
Will you be mostly scanning documents in one or multiple languages? If the latter is the case, you’ll want OCR software with a detailed range of dialect capabilities. That becomes especially important when the documents in question involve Japanese or Arabic, which differs in structure from normal language conventions.
OCR software is made possible by computer vision and machine learning.
Computer vision enables character identification while machine learning facilitates the algorithm to learn from foreign documents and improve detection rates.
2. Document Classification Ease
Do you have some sort of filing system in place for your business?
Perhaps you group data according to certain client metrics to create some sort of structure to your data management strategy, so employees find what’s needed fast and easy. Maybe your system is a lot more unstructured than this.
Either way, a good OCR software solution should be able to facilitate Document Classification. Hence, there’s no need for manually categorizing the documents after conversion.
But document classification alone isn’t enough. Businesses need to dive deeper to ask vendors about what method of document classification the specific OCR in question uses.
Generally, there are three methods of document classification namely:
- Rules-based grouping
- Unsupervised method
- Supervised grouping
Good OCR tools typically work on a rule-based method, featuring natural language processing algorithms. The concept is to mimic how we would sort out documents by identifying phrases or keywords and then placing these files into one folder or the other.
By analyzing segments in the text according to semantic relevance, this type of OCR software enables automatic categorization.
3. Reliability in Noise
How is the OCR tool’s ability to work in an environment where input quality is not the best?
Generally, you’d want OCR software that performs well in ideal circumstances.
But, even more crucially, it should be able to give high accuracy or reliability when the image or input material suffers from degradation.
Things that could lower the image quality making it hard for conventional OCR software to read characters, include scratches and cracks in the background.
A blurry quality or low resolution may also hinder the success of OCR readers.
Other OCR noise factors include the amount of lighting available and secondary character sources.
Therefore, good OCR software should be reliable in situations of image degradation, and generally in out-of-training situations.
For example, the image distance from the OCR lens may vary, and so too does the angle of the reading, among other geometry changes.
The ideal OCR tool should be able to offer successful reads when the field of view parameters change, causing characters to appear a lot different even if these are among the fonts for which it is trained.
4. Ease Of Use
Even the best OCR tool is not very ideal if it has a complex Application Programming Interface (API).
Hence, there should be a preference for an easy-to-learn API. This is made possible by a Rapid Application Development process, in case the developer will be creating or finetuning your OCR package from scratch.
Additionally, with a simple API, integration with other elements in a larger system or applications becomes easier too.
By being able to support several programming languages, from C to VB, etc, the need for interfacing across multiple languages is also eliminated.
Beyond the API, the underlying algorithm, and specifically the parameterization, is also an important factor when choosing OCR software.
A well-defined parameterization entails breaking down parameters in short and distinctly configurable segments, enabling a misconfiguration in one part from affecting the rest of the code.
5. Measure Of Efficiency
How do you measure efficiency in your company?
Speed or turnaround time is a priority for OCR-based software, but it’s an even bigger concern in certain sectors or lines of business. That’s especially true in a busy environment, where companies need to maintain a high level of productivity.
For instance, in product defect identification systems, you’d want an OCR reader with relative accuracy but high processing time. A holdup in such a system would cause drastic consequences in terms of output.
What does OCR efficiency mean to you?
The answer differs on a case-to-case basis, so you’ll want to create a metric of efficiency according to the nature of your company’s workflows. If you deal with huge volumes of paperwork, the scannable documents per period of time may be your most important factor.
On the flipside, speed may not be the biggest issue in circumstances where good image quality is a measure of efficiency.
The ability to timeout when image degradation is beyond salvageable and quickly move on to the next image may also be a priority in some cases.
Again, OCR efficiency depends on the condition of use in your business.
Conclusion
So what are the ideal features of excellent OCR software?
Well, let’s recap the OCR selection criteria we’ve discussed today for a brief and succinct reminder on what you’ll be looking for:
- Conversion range: what character formats are supported
- OCR classification: if classification is available, how does it work
- Noise performance: how OCR performs in untrained situations
- Efficiency: This may be dependent on error handling, speed, or reliability. Consider your specific workflow case.
The most desirable OCR software tools are generally AI-powered and offer intelligent data extraction, reliant on machine learning and natural language processing.
Intelligent Data Extraction means that the OCR tool goes beyond simple data entry, offering supporting features to enable the full-scale automation of document processing into an IDP platform. Lastly, be sure as well to take a look at client testimonials, which offer a nice measure of proof when considering your OCR options.
