How does NLP help with Text Recognition?
How does NLP help with Text Recognition?
Optical Character Recognition (OCR) is a common way to get information from scanned documents. Workflows and business processes have changed a lot since companies started using technology. By making OCR more accurate, you can get better results regarding how well it works.
As you might expect, the quality of the images used to train an OCR solution affects how well it works. One problem with using OCR solutions in the real world is that the accuracy of words drops significantly as characters' accuracy increases.
Using NLP (Natural Language Processing) techniques to replace wrong words with correct ones is one way to improve the accuracy of words.
In this post, we will answer questions like how natural language processing (NLP) is used in text recognition and how NLP improves text recognition.
What is NLP?
To make computers as intelligent as humans, natural language processing (NLP) is a subfield of computer science and artificial intelligence (AI) that focuses on translating written and spoken language into machine understanding.
Natural language processing (NLP) combines statistical, machine learning, and deep learning models with computational linguistics, which models language using rules. When used together, these tools allow computers to "understand" the whole meaning of human language, including the speaker's or writer's purpose and mood, in the form of text or audio data.
Natural language processing lets computers translate between languages, follow spoken directions, and quickly summarize vast amounts of text, often in real time. You've probably already used NLP in voice-controlled GPS devices, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But natural language processing is also becoming more critical in corporate solutions that aim to make businesses more efficient by automating and standardizing processes essential to their success.
Add to Your Reading List: Beginners' Guide to NLP in 2022
OCR (Optical Character Recognition) & Text Recognition
Automatic text recognition relies heavily on optical character recognition (OCR). Businesses' need for scanning and digitizing paper documents has driven the development of optical character recognition technology.
Business operations must manage various documents, such as letters, invoices, printed contracts, and images. When there are a lot of records, even simple things like searching can take a long time and cost a lot of money. OCR software can scan paper documents and convert the extracted data into digital, organized representations. The data may then be processed further, and operations like sorting, searching, and editing can be performed rapidly.
Businesses of many types use OCR software. Banks' procedures for cashing and processing checks provide a good illustration. Processing a review electronically (via scanning, text conversion, and signature matching) is a time saver for the bank, the payor, and the recipient—case in point: the ability to conduct a global search of voluminous legal papers. OCR technologies can process massive numbers of documents and provide instantaneous access to data. Companies in the energy industry, which serves a vast client base, might also benefit from accounts payable. A common way to get invoice data ready for electronic processing is to scan them and save the data as key-value pairs in a database.
Naturally, examples may be found in every field imaginable. When it comes down to it, OCR technology changes the game for how organizations use and manage paperwork. Once the information from digitized documents is stored in a database, it can be searched, modified, and even translated.
If you are interested in text recognition, read the following articles:
- Top 5 Text Recognition Applications in 2022
- What is Optical Character Recognition (OCR) - The Definite Guide
- The A-to-Z Guide to Text Detection
How is NLP Used in Text Recognition?
However, OCR has the drawback of not being able to provide any further information about the papers it processes. Let us give you an example: Imagine asking a Spanish translator who knows nothing about baseball to interpret a baseball broadcast into English using optical character recognition. The words could be translated, but without the context, the translation may not make much sense. If you did not know what a "double-play" was?
With the help of Natural Language Processing (NLP), computers can "understand" what's written by analyzing the words and phrases inside them. It may obtain valuable information and insights from the source files when properly implemented.
Applying optical character recognition with natural language processing to electronic documents is a potent combination, especially considering the widespread usage of faxes in many fields.
In addition, in order to analyze the data contained inside these documents, it is necessary to scan them using text recognition technology. NLP improves this process by letting these systems recognize relevant concepts in the resulting text. This helps with the machine learning analytics needed to decide whether an item should be approved or not.
How can NLP Improve Text Recognition?
Now that we understand these two technologies let's briefly look at how NLP technology can improve text recognition.
Optical character recognition uses technology to tell the difference between printed or handwritten text characters in digital images of physical documents, like scanned paper documents. Text recognition stands for optical character recognition, and text recognition can find words in an image that is being scanned, but it can't figure out what those words mean.
NLP comes into play at this point!
Natural language processing lets computers understand written and spoken words in a way that is similar to how people do. Let's imagine that these two technologies have joined forces!
NLP can improve the accuracy of text recognition and help this technology understand the text in the same way human beings can.
But how do OCR and NLP help your business?
Modern apps powered by OCR and NLP allow your business to do a wide range of document-related activities, including but not limited to the following:
- Identifying documents like passports and ID cards may be read mechanically.
- You can quickly scan documents, including bank cards, invoices, tickets, and checks.
- Fill in billing information automatically.
- Transmit information to a customer relationship management system or online form automatically.
- Multiple sources of client information must be checked for accuracy.
- Businesses that choose data extraction services have access to summarized data that can be used to make more trained choices and go forward with confidence.
In a nutshell, OCR, often known as text recognition, is the process of digitizing text from images of printed text. However, this technology can not understand the meaning of the text. Processing of natural languages provides computers with the capacity to comprehend written and spoken language in a manner that is comparable to that of a human. So, NLP can improve text recognition accuracy by helping understand the meaning of the text.
In recent years, OCR has developed into a very helpful resource. If you need text recognition technology to improve your business operations, Cameralyze is here to help you! Cameralyze's AI-based solutions simplify businesses' analysis and extract critical data using technologies like text vision, data validation, face recognition, and document identification.
Cameralyze is an AI-solutions platform that does not need any coding. The platform provides access to almost all AI and computer vision-based technologies at a low cost, and text recognition services are also available on the platform.
Importantly, unlike many of its rivals, the platform does not need any special software or technological know-how on the user's part. You can access it directly using a web browser and simply scan your document and upload it to the site to get instant text. To put it simply, Cameralyze provides you with the adaptability you want and the freedom to use your data as you see fit. It helps you make the most of your digital data.
Can NLP and OCR solutions be built for your business use case? How to begin using an NLP solution? What tasks can be automated with OCR? Do you have any other questions?
Start now, and try text recognition solution of Cameralyze now.
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