Digitize Handwriting With Intelligent Character Recognition

Intelligent Character Recognition

A lot of people use the terms “machine learning” and “artificial intelligence” interchangeably. And while they are very similar, this perspective acknowledges just one small part of a much larger story.

Machine learning is actually a very particular subset of AI which employs statistical techniques to give computers the ability to “learn” on their own. Essentially, the more relevant data you feed a computer, the better it’s able to do just that – all without explicitly programming it to do so.

We’ve already seen the effects of machine learning across nearly every aspect of business that you can think of. When you purchase something on eCommerce websites like Amazon.com, they immediately greet you with recommendations for other products you might enjoy. As you make more purchases, those recommendations become more specific – and accurate. This is because the machine learning model has more data from your purchase history to draw from. And, as a result, it is in a better position to recommend inventory items that might interest you over time.

The larger benefits are equally important. Machine learning creates an opportunity for better customer segmentation and accurate lifetime value prediction in terms of marketing. Furthermore, it can create better and more holistic SPAM detection when it comes to email operation. It can even increase the efficiency of predictive maintenance in the manufacturing industry.

But just as AI gave way to machine learning, machine learning has given birth to new advancements of its own. One of those takes the form of intelligent character recognition, otherwise known as ICR for short. Though this may seem like a simple concept, the potential implications are anything but. This is particularly true when it comes to running our businesses and the decisions we make as a result.

Intelligent Character Recognition: Breaking Things Down

In the field of computer science, intelligent character recognition actually refers to an advanced form of optical character recognition, or OCR. On the surface level, it’s a technique that can automatically digitize handwriting – regardless of the fonts or even the different styles of handwriting that may be present.

The important part of this – and the part that ties back into machine learning – is the fact that most ICR solutions have self-learning systems called neural network that automatically update the “recognition” database as new handwriting patterns are identified. So the more content you feed into the system – and the more types of content you can provide – the more accurate that neural network (and thus your results) become.

In the world of business, this is potentially a huge benefit. Companies that deal with a wide range of different vendors, for example, often deal with massive amounts of handwritten paperwork. If they don’t get around to manually entering that information into their digital systems (or if they do it incorrectly), this could lead to double payments, billing errors and more.

Property management companies usually have prospective tenants fill out rental applications by hand. With the right ICR solution, those forms would no longer have to undergo processing by hand and could instead instantly enter a database. The data from the forms then becomes searchable and compatible with other solutions.

But the running theme throughout all of this is that ICR is a perfect opportunity for most businesses to automate (and often outright eliminate) the redundant administrative work that is typically required of employees, allowing those valuable people to focus more of their attention and energy on forward-thinking initiatives within the organization.

The Filestack Approach

At Filestack, we believe in the power that only intelligent character recognition can bring to the table – but we also believe it is our duty to take things one step further, too. Our ICR solutions put you in a better position to automate data entry for critical documents, give you a chance to translate complete physical books into electronic versions, extra information from important documents like business cards and more, but we’re also able to create a custom model built from the ground up with only your organization in mind.

The best part is that this requires absolutely no work on your part. You sit back, we learn your business and we create the custom model that is right for you – making it easier than ever to generate the results you need when you need them the most.

Reach out to start a conversation on intelligent character recognition, or browse our full list of OCR capabilities.

FAQs Related to ICR

What is the difference between OCR and ICR?

The main difference between the two is that OCR recognizes text in files and translates them into a machine-readable format. On the other hand, ICR recognizes human handwriting in images or other files. As stated earlier in this article, ICR is something like a specialized OCR that empowers a system with handwriting text recognition features.

How does ICR work?

ICRs use AI and ML algorithms to recognize handwriting in images. The process typically involves several steps:

  1. Image pre-processing: The algorithms cleans and processes the image to enhance the quality of the handwriting. This includes removing noise, adjusting brightness and contrast, and binarizing the image.
  2. Segmentation: The algorithms divide the image into several sections, such as lines and words. This makes it easier for the ICR to recognize individual characters.
  3. Feature extraction: The system extracts features from the partitioned sections, such as stroke width and direction. This helps represent the handwriting in a machine-readable format.
  4. Recognition: A machine learning algorithm (e.g., a neural network), undergoes training through a dataset of handwriting samples. This helps ICR to recognize the characters based on their features.
  5. Post-processing: The ICR scans the recognized text for errors, such as missing or misrecognized characters. It may also correct these errors if necessary.

Sounds complex, right? That’s because it is. ICR systems usually involve more complications and resource consumption compared to OCRs. That’s because they need to handle recognizing different types of handwriting.

What is an ICR tool?

An ICR tool refers to a software application or service that has ICR functionality. These tools can range from standalone apps to web apps, or services that you can integrate. The best file management services usually include ICR tools, along with OCR, in their arsenal.

What is the difference between OCR and AI?

OCR and AI are similar topics; however, they’re not the same. OCR refers to a technology that uses specialized algorithms to recognize text in images, converting text into a machine-readable format. On the other hand, AI is a much broader term. It refers to the study or practice of simulating human intelligence in machines. Some tasks that AI can do are natural language processing, image recognition, problem solving, and more. Practically, AI can do a lot more than OCR. Nowadays, people use AI for self-driving vehicles, chat bots, and a whole lot more.

Where do we use ICR?

Businesses use ICR for a lot of scenarios these days. These include data entry, document scanning, forms processing, handwriting analysis (psychology or forensics), signature verification, education, transcription, and more.

Can OCR recognize handwriting?

It’s possible for OCR to recognize handwriting, but it will take a lot of resources and time compared to using ICR. OCR’s primary purpose is to recognize text in printed documents. Because of this, it’s not as effective in learning handwriting compared to ICR. The variety in handwriting (e.g., cursive) also contributes to the difficulty that OCR might experience when recognizing handwriting. So, when it comes to handwritten text recognition, ICR remains the more accurate and efficient choice.


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