Handwriting Recognition — An Innovative Technology Using Machine Learning
Ways to Handwriting Recognition Using Machine Learning
We all must have thought at least once that having beautiful and legible handwriting could have fetched us more marks in school. This makes us wonder what if there were machines that could recognize Handwritten text; yes! Handwriting recognition is now possible using machine learning techniques.
Transcription of vast amounts of handwritten data is challenging and consumes a lot of time. Optical Character Recognition (OCR) technology can convert printed documents into readable textual data for machines. Today OCR has expanded to many industries, including healthcare, banking, and research. The OCR market is estimated to reach 13.38 billion US dollars by 2025, with a yearly growth rate of 13.7%.
Handwritten Text Recognition (HTR) is an active research branch in artificial intelligence. With advancements in Deep learning technology, there has been fast progress in developing Handwriting Recognition Technology. However, there are certain practical challenges in recognizing handwritten text. Before diving deep into Handwriting Recognition let’s learn about Handwriting Recognition technology.
What is Handwriting Recognition?
Handwriting Recognition is a technology where computers can recognize and interpret handwritten text into machine-readable data. Handwriting Recognition is mainly based on computer vision and pattern recognition. The handwriting text might be in the form of scanned documents or images. Handwritten Character Recognition is called Intelligent Character Recognition (ICR) as it requires more advanced machine learning algorithms than OCR.
There are two types of handwriting recognition systems: online and offline systems
Online Handwriting Recognition System
This method involves the conversion of text written on a digital pad using a sensor-based digital pen; the digital pad picks up pen pressure and stroke movements as data points. Pen movement information is used to evaluate handwriting based on line quality, writing speed, and writing style.
Offline Handwriting Recognition System
This method involves the recognition of handwritten text from images and scanned documents. It doesn’t require any stroke information to recognize text. The data is directly obtained from static images. This system is in demand to digitalize historical and research data.
Steps in Handwriting Recognition
Handwriting Recognition systems always face a lack of training data, and without training data, it is impossible to create models of different languages and handwriting patterns. To overcome this problem, Neural network models are developed based on Handwritten Character Recognition algorithms. This can be divided into four stages:-
- Image Pre-Processing
Image Pre-Processing is a crucial stage of handwriting text recognition, which is very helpful in accurately predicting handwritten text. The primary input here is an image and performs several functions such as noise removal, image segmentation, cropping, cleaning, scaling, and more.
- Segmentation
In this stage, the image is segmented into sub-image of an individual image which consists of a sequence of characters. Here each character is resized into 30x20 pixels.
- Featured Extraction
The input data involves measurable properties such as stroke angles, directions, and series of tilts; extracting these featured data is used to classify the data. The main aim is to identify features that are not dependent on other factors.
- Classification and Recognition
This is the final stage of handwritten recognition involving decision-making. Classification is done by training algorithms using a log sigmoid activation function. Neural networks play a significant role in handwritten text recognition.
Challenges in Handwriting Recognition
- The differences and ambiguity in handwriting styles vary from person to person, posing difficulty in creating patterns.
- Large volumes of written data in different languages and varying scripts in different locations make the scope of handwriting recognition limited.
- The handwriting style of a person is inconsistent as it varies from time to time
- Cursive writing makes the separation and recognition of characters a challenging task. Slanted writing towards left and right creates an ambiguity in understanding an individual’s handwriting.
- The quality of written documents degrades over time, which results in poor image quality. Machines cannot make accurate predictions with text drawn from poor images.
Use cases of Handwriting Recognition technique:-
- Banking
In the banking sector, most money transactions happen by filling challans, cheques, and forms. Recognizing this textual data and entering the information manually consumes a lot of time. Handwriting recognition systems make it easier, particularly with handwritten digit recognition patterns.
- Insurance
Insurance companies receive a massive amount of documents on a daily basis, and delay in scrutinizing client claims can impact the company’s performance. Automation of handwriting recognition software reduces the burden owing to the fast claims processing.
- Medicine and Healthcare
It’s challenging to understand doctors’ prescription slips. Using handwriting recognition software makes it easier for pharmacists to understand and encrypt. Digitalizing patient enrollment forms with handwritten text recognition is essential to store valuable information about patients.
- Libraries
Libraries have a large number of historical and scientific books. Transcription of this valuable data takes humungous manual labor and time. The scanned images of the textual information should be helpful to classify, index, and recognize. Handwritten Text Recognition (HTR) facilitates easier online transcription, translation, and publishing of these documents.
Final Thoughts
We hope you understand the scope of Handwriting Recognition technology. The research advancement in machine learning and deep learning has boosted this technology to greater potential. There is no doubt that handwritten recognition will reach a good market position like OCR in the coming years.
If you are looking for more information and guidance about machine learning projects, the Advanced Artificial Intelligence and Machine Learning program will help you better understand OCR and HTR with technical knowledge of Machine learning algorithms.
