What To Look for in an OCR Mobile App and SDK

By March 5, 2018 Blog
ai-ocr

If you are considering an optical character recognition (OCR) app for your business, there are a few considerations that you should take into account that can help you sift through your options. Understanding the differences between different types of OCR apps, the platforms that they run on, and how they will mesh with your current business processes is important for finding the right solution for your business.

 

Some of the things you should take into account include understanding the differences between traditional and AI-powered OCR apps, knowing which platforms will be the best fit for your business, and taking other requirements into account that may be specific to your company. As you weigh your options, take these considerations into account:

 

Traditional OCR vs. AI-Powered OCR Apps

 

If you haven’t used an OCR solution in the last five years or so, there is a good chance that you used a traditional OCR app. Traditional OCR apps provide the same basic functionality — recognizing and categorizing data — but there are a few key differences that you should be aware of between traditional and AI-powered OCR apps.

 

OCR apps that are driven by artificial intelligence are capable of improving over time. They familiarize themselves with the type of documents and data that you are feeding into the system and use that experience to improve the quality of the data over time. AI makes OCR systems, which are already a big improvement in terms of accuracy over manual-entry, even more impressive and reliable for data collection. Their ability to self-learn allows them to custom-tailor their processes to your company’s files and data types.

 

OS and Software Platform Considerations

 

With OCR apps, the OS and platform that you are using is an important consideration. If your company provides mobile devices to your workforce, what OS do the devices use? You have to make sure that the OCR system that you choose provides a high-quality app for that particular operating system. Most apps will offer versions for both iOS and Android devices, but there may be differences in the quality of the app.

 

If the app that you are interested in comes with a free trial, it’s always a good idea to try it out and see if it would be a good fit. It can be difficult to gauge the quality of a mobile app (or even an SDK) without trying it out first.

 

A Clean and Consistent User-Interface

 

A primary consideration for any AI-powered OCR app that you choose should be a clean and consistent user-interface. If you will have many employees using the app, an app with a simple design will limit the amount of training and re-training you’ll have to provide to begin using the app in your business processes.

 

A clean interface also helps to cut down on mistakes and usage errors, while improving the speed of data capture. It may be a good idea to roll out the app to a small test group within your company to collect feedback before making a decision that could affect your company as a whole.

 

Language Support

 

Does your company collect data in multiple languages? If you do, it is critical that you find an OCR app that offers support for every required language. You don’t want to have a different set of processes for collecting data in each language if you can avoid it. Additionally, you’ll want to make sure that there isn’t a big difference in the quality of data capture between different languages. Xtracta supports dozens of languages, making it the perfect solution for companies that work with customers worldwide.

 

Accuracy and Flexibility

 

An OCR system’s feasibility is determined by its accuracy. How accurate is the OCR app? Is it able to parse and collect data from many different document types? Take into account the main sources of data within your company and make sure that any app that you choose is able to collect data from those file types and others, giving your company the flexibility it needs to expand without worrying about how you will collect new types of data.