Optical character recognition or OCR technology is used worldwide either independently or as a subset of some service to extract information from documents.
Technological advancements are continuously innovating the world as we know it. Most of our routine tasks are taken care of by some subset of technology.
Moreover, the interaction between humans and these technological advancements has also increased. Thus, to communicate better, humans and their scientific creations need to be on the same page.
It becomes a difficult task to impart information to the machine for the purpose of processing that data. Therefore, this issue needed to be addressed, and OCR technology presented a timely solution.
In this article, we will go through the engine running behind the OCR technology and which factors differentiate the best one from the bunch.
What Does the Term OCR Technology Refer to?
To state it simply, the Optical Character Recognition – OCR technology is a scientific innovation used for data entry and extraction from scanned documents. It can recognize textual information as well as pictorial data.
Moreover, it deals with different forms of documents, i.e., handwritten, printed, etc. Even if the document has gone through inevitable wear and tear, it is no hassle for the OCR technology.
After recognizing and extracting information from documents, the optical character recognition system translates it into machine-readable text. In this way, the communication barrier between humans and computers is eliminated to enable the streamlining of data processing.
Engine Running Behind the OCR Technology
To better understand how OCR technology does its magic, let’s delve into the details about the technologies behind the end product. The optical character recognition software runs on top of the AI engine.
Moreover, a couple of AI-based tools, i.e., Convolutional Neural Networks (CNN) and Natural Language Processing (NLP), also assist the character recognition process.
Artificial intelligence and machine learning have their roots in every futuristic innovation we see in the recent era. Be it self-driving cars or digital biometric verification; AI is the engine behind them all.
Similarly, when it comes to swift and accurate data recognition and extraction processes, AI takes care of that remarkably. Hence, the redundant, time-intensive, and repetitive tasks related to documents and photos are also automated by AI-based OCR technology.
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN) feature is used to recognize a picture and identify digital patterns. Then, the conversion of these patterns into text takes place. Here, the OCR technology draws from AI to remove any undesired streaks, lines, and spots from documents to streamline the character recognition process.
Moreover, the advanced mathematical algorithms behind the OCR technology enable Convolutional Neural Networks (CNN) to identify all the alphabets and symbols.
Natural Language Processing (NLP)
When the Convolutional Neural Networks (CNN) are done performing their magic, then the application of Natural Language Processing (NLP) algorithms assists the character recognition process. They analyze the information logically and look for relating patterns between different alphabets.
Moreover, it allows the user to maintain and edit the original layout, font, color, etc., of the document. Thus, it must be said that the benefits of OCR technology for different organizations seamlessly automate the redundant chores.
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Essential Features to Look Out in OCR Software
The use of OCR technology ranges from open-source to commercial programs, which can be tailored to fit the needs of different users. From the banking industry to ID verification, OCR is being used in a variety of ways.
There is a long list of organizations that deal with user documentation daily and need to save their information digitally. So, for all these organizations, there is the same checklist of features that they should look for while employing OCR software services. A few of these defining features are outlined below:
- Support for e-documents, handwritten notes, and digital pictures.
- The accuracy rate should be over 90%.
- There should be multiple options for export format, file compression options, operating system compatibility, etc.
- Should have cell phone support
- The higher the range of supported fonts, the better will be the OCR service.
- Should support all the official languages around the globe.
- API and integration capabilities.
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Featured Image by Gerd Altmann from Pixabay