A Deep Dive into the Meaning of Handwritten Text Recognition with OCR

Technology is making the impossible possible. Today, you don’t have to be worried if you have to convert a handwritten text into digital text. There are tools that can help you convert your bills, insurance documents, etc., quickly.

OCR (Optical Character Recognition) has been successfully implemented to convert handwriting into digital text. Artificial Neural Networks (ANNs) that comprehend patterns from a group of examples play a major role in the process. 

Today, we will dig deep into handwritten text recognition online (HTR), understand how OCR scanning is done, and more. 

Let’s get started!

What is Handwritten Text Recognition with OCR?

Simply put, handwriting OCR converts handwritten text into machine-readable text. Even a few years ago, handwriting recognition seemed a distant reality. However, with the advancement of technology and the implementation of newer OCR models, it has become easy to convert both machine-printed and handwritten documents. 

On that note, let’s delve deep into OCR technology and understand how it reads text, its evolution, and what the future holds for OCR software.

How OCR Reads Text?

However, converting handwriting to text is not as easy as converting printed text. Since everyone has a different writing style, there’s no way to identify and use a standard shape for conversion. This challenged the software to act with the same precision as printed text. 

Recent developments in machine learning and Artificial Intelligence allow the software to read and convert handwritten text into digital data. Here’s how it works: 

A. Image Preprocessing

The software first scans and captures an image of the document presented. Then, it processes it to enhance its quality. 

B. Feature Extraction

OCR analyses pen strokes, direction, and thickness to understand each character’s unique features. It also checks the spacing lines for accurate conversion. 

C. Handwritten Character Recognition

The tool uses machine learning models, such as neural networks or hidden Markov Models (HMMs), to compare extracted characters with a database of similar patterns and identify the best match. It also uses information about similar elements to improve recognition accuracy.

D. Output Generation

It then converts recognized characters into digital text format and delivers a searchable PDF document. 

The mechanism has evolved over the years and has made things easier for everyone. 

In the following section, we will learn what the future holds for its software.

What is the Future of Handwritten OCR?

A. Implementation of Deep Learning Techniques

Deep learning techniques like Convolutional Neural Networks (CNNs) will improve the mechanism and make it more important. CNNs have proved to be the best at extracting intricate features of images, enabling OCR to handle various document layouts and fonts accurately. 

Adding on, using recurrent neural networks (RNN) will further enhance sequential pattern recognition in texts, making offline and online handwriting recognition system more accurate. 

B. Real-Time Optical Character Recognition

The future holds many possibilities for real-time human handwriting recognition. Presently, features on mobile devices like Google Lens recognize handwriting and transcribe it correctly. This works when you use them to translate street signs or scan a receipt to extract the total amount. 

The future looks bright, and things will become even more accessible. 

C. AR/VR Integration

Augmented and Virtual Reality are not new to anyone. While most of us know they offer an amazing experience, only a few know how handwriting text recognition plays a crucial role. 

OCR ensures real-time text translation within the user’s field of view, and this will become more effective in the future. 

D. Improved Handwriting and Font Recognition

The technology has improved a lot over the years and is expected to recognize diverse font styles and handwritten content more accurately. This will automatically improve usability and reliability in the identity verification process. 

E. Optical Character Recognition Automation

Automatic conversion of text from images is currently common. It is used in banking, insurance, accounting, healthcare, and logistics. It is expected to develop further in the coming days to make conversions easier. 

F. Improved Data Extraction

With time, this is expected to grow and seamlessly extract data and analyze datasets from diverse ID types. These will broaden the scope of recognition systems and also improve their depth for better data analysis.

The system analyzes texts using general handwritten text databases to ensure accurate results. 

Before we end the discussion, let’s take you through some of the best use cases of document analysis and recognition systems.

What Are Some of the Best Use Cases of Handwriting Recognition?

A. Historical Documents Preservation

Historical documents need to be preserved to keep records and for future use. Using this system to recognize and digitize handwritten text online can help create archives and digital libraries. 

B. Healthcare and Legal Sectors

Data retrieval is an essential aspect of the healthcare and legal industry. The reason is these industries mostly rely on handwritten notes. It is crucial to digitize them for easy data retrieval and improve record-keeping. 

C. Education

This system eases document analysis and recognition. Advanced tools like TrOCR can be of great help in education. It helps digitize assignments, quizzes, and notes. Consequently, it allows teachers to keep records and analyze students’ work. 

D. Banking and Financial Services

The system improves the processes and helps in speeding up the process of transactions. Handwritten bank documents can easily be converted to digital texts using this system.

E. Insurance

Companies can automate handwritten claims using this system. This way, insurance claims can be sped up.

What Makes AnnotationBox the Best Company for Digitizing Texts?

Annotation Box comes with experience of 6+ years in providing data annotation services in computer vision, data processing, and content moderation. We have all the resources to ensure smooth processes and accurate results. Here’s a list of services we offer: 

• Image annotation
• Video annotation
• Text annotation services
• Audio annotation
• Content moderation
• Product categorization
• Geospatial annotation
• Medical annotation
• Data collection services
• Data de-identification services
• Generative AI data solutions

You can contact us for our services to get the best help.