How is AI Used in Handwriting Recognition?
Have you ever felt like screaming while copying notes from your friend's notebook? Just because the handwriting was hardly understandable.
I am sure, at least once, you thought that there was a tool that could identify such messy handwriting.
Well, in your childhood, it might have been a sci-fi dream, but now it's a reality. Yes, extensively messy handwriting can now easily be recognized in a few minutes. Thanks to several AI-powered handwriting recognition tools.
The fusion of two technologies, Optical Character Recognition (OCR) and artificial intelligence (better to say, deep learning), can recognize any kind of handwritten text. One such creative development in the Optical Character Recognition (OCR) field is handwriting recognition.
OCR technology converts texts in printed or physical format into a machine-readable format. This helps various industries cut down their labor and time costs by a large amount. As a result, the market value for OCR technology is expected to reach 13.38 billion dollars by 2025, with a 13.7% yearly increase.
A rapid increase in digital processes in industries has helped OCR technology reach such potential. As a result, industries have already understood the advantages of using OCR for handwriting analysis. But even with all its advances, OCR faces a major challenge in its way, that is, handwritten text recognition.
Before diving into the problem areas, let's first understand what handwriting recognition means.
What does handwriting recognition mean?
Handwriting recognition is the process of developing applications capable of analyzing and recognizing the handwriting patterns of humans. What makes handwritten character recognition challenging is the massive amounts of data presented in the form of hand-writings.
Every human being's handwriting differs from another's, and it also changes from time to time. Therefore, it is a much more complex task to recognize the handwriting of billions of people worldwide. In addition, the process of handwritten text recognition faces many other challenges.
Deep learning has, however, provided a faster and more accurate way to approach this problem. For example, a handwriting recognition engine uses computer vision and deep learning technologies to understand different handwriting. It enables the algorithm to learn different handwriting, just like humans do.
As deep learning algorithm are much more reliable, handwriting recognition has become easier. But we can still face some issues while implementing the algorithms in pattern recognition techniques.
Problems in converting handwriting into text:-
The differences and ambiguity in every person's handwriting are major issues. Everyone has varying strokes, character spacing, irregularities, and handwriting styles, making it a complex task to identify an individual's penmanship.
Changes in a person's handwriting style over time can also be a big differentiator. Many people's patterns of handwriting and style modify after sometime.
Over time, the quality of the document degrades. Especially for handwritten texts, processing these types of documents produces noise and poor image quality.
Variability in handwriting choices, e.g., some people may write in cursive, or some may write in a straight line manner. It is difficult for software to analyze a different writing style than it has seen before.
Benefits of handwritten digit recognition
1. Optimal data storage
Documents such as file records, contracts, etc., have handwritten signatures or notes that need to be stored. Converting them into electronic texts using handwritten text recognition technologies can provide a better way to store the files. It also saves on time, space, and costs required in the process.
2. Access and retrieval
We can access the data efficiently by storing information using handwriting recognition software. You can easily retrieve any information by entering the name/details and searching for the file on the system. Therefore, it becomes a much faster implementation than manually accessing the files.
Use cases of handwritten character recognition.
The implementation of handwritten recognition using deep learning in many industries is not just a thought these days. As the number of applications increases, the demand for handwritten text recognition also rises.
1. Medical and Healthcare applications
The digitization of patient prescriptions and enrollment forms saves time and effort by improving the user experience. However, as it's difficult to read a doctor's note, many patients receive the wrong dosage or medicine, which can lead to adverse effects.
With the help of the handwriting recognition model, doctors can provide quality healthcare by reducing these risks. These are helpful in places where fewer doctors are available to provide each person with individual care.
2. Banking and Insurance applications
In most banking sectors, people provide handwritten cheques, which can become a concern to deal with. Moreover, the insurance industry also collects many claims; without proper settlement of these documents, the company can receive a massive blow.
As compared to manual efforts, software seems to be a much faster and reliable option while implementing handwriting recognition on any documents. As a result, dealing with large amounts of cheques and documents will be quicker for both the employees and the users.
3. Libraries and Archives
Providing digital versions of books and historical documents/records for customers online is a time-consuming process. Manually documenting, indexing, and publishing the records would take forever.
But HTR (Handwriting Text Recognition) provides a way to automatically publish and transcript online more easily and reliably.
Like this, there are other areas where efficiently using HTR technology improves accuracy.
How does handwriting recognition work?
There are two ways handwriting recognizing patterns work:
A digital sensor pad or a digitizer picks up the movement of the pen as the text is being written. Using this data, we analyze the words/characters written. In addition, online methods usually evaluate features such as writing speed, letter execution, and line quality to predict the text.
The data presented in offline methods consists of snapshots of handwritten text. These are given directly to the system for analyzing the texts. This form of recognition is usually used in analyzing archival or historical documents.
Steps in handwriting recognition:-
A handwriting algorithm contains steps to identify the texts from the given data input. These apply to machine learning algorithms, techniques, models, and recognition system strategies. They are
Image pre-processing is essential in text recognition since it helps make the other process easier. Operations such as noise removal, segmenting images, cleaning, cropping, etc., are a part of image pre-processing. This process removes any unnecessary noise from the image and helps identify the objects in the image that may be useful.
It is a complex process where the algorithm segments images into smaller sub-images containing individual characters.
3. Feature extraction:-
The properties identifying the data present in the image are measured in this step. It helps identify relevant features and extract those not dependent on one another. The image is detailed using a feature extraction algorithm to provide accurate character identification.
4. Classification and recognition:-
In this final step, we use neural networks or other classifiers to decide on the input data provided.
So, this is how handwriting recognition works. It's just the basic.
Over the past few years, we have seen remarkable development and enhancement in Handwriting recognition techniques, and it is still in its infancy. The machine learning techniques have really boosted the process, and with the upcoming trends, we can see a surge in the advancement of HTR which involves neural networks and deep learning algorithms.
Similarly, if you want more information and guidance based on machine models and algorithms, check out the AI and ML Advance Program, which will help you better understand and grasp the HTR techniques and other recent technological developments.