Uses of Machine Learning Handwriting Recognition

We explore how machine learning techniques can tackle handwriting recognition problems

Recent Deep Learning advances such as the introduction of transformer topologies have accelerated our progress in handwritten character recognition. Intelligent Character Recognition (ICR) is a word used to describe the process of recognizing hand written content. The algorithms used to solve ICR require far more intelligence than those used to solve ordinary OCR.

We will learn about the task of handwritten text identification, its complexities, and how we might tackle it using machine learning and deep learning techniques in this post.

Machine Learning Handwriting Recognition Applications

Healthcare and pharmaceuticals

In the healthcare/pharmaceutical industry, patient medication digitization is a serious issue. Roche, for example, processes millions of petabytes of medical PDFs every day. Patient enrollment and form digitalization are other areas where handwritten character detection has a significant influence. Hospitals and pharmaceutical companies can greatly improve customer experience by adding handwriting analysis to their toolbox of services.

Insurance

A huge insurance firm receives over 20 million documents per day, and a claim processing delay can have a significant impact on the organisation. The claims document may contain a variety of handwriting styles, and relying solely on human processing to handle claims can significantly slow down the pipeline.

Banking

People write checks on a regular basis, and they continue to play a significant part in the majority of non-cash transactions. The current check processing technique in many developing nations involves a bank staff to read and manually put the information on a cheque, as well as verify the data such as signature and date. Because a bank processes a huge number of cheques every day, a handwriting textual recognition system can save money and hours of human labour.

Online Libraries

Huge volumes of historical knowledge are being digitised and made available to the world by uploading image scans. However, this endeavour will be ineffective unless the text in the photographs can be detected and indexed, queried, and browsed. Handwriting identification is essential for bringing mediaeval and twentieth-century papers, postcards, and research works to life.

Techniques

ML methods such as Hidden Markov Models (HMM), SVM, and others were used in the early attempts to solve handwriting recognition. After the initial text has been pre-processed, feature extraction is used to detect crucial information about each character, such as loops, tipping point, aspect ratio, and so on. To obtain the results, these created features are passed into a classifier such as HMM. Due to the manual feature extraction step and their low learning ability, machine learning algorithms' performance is quite constrained. Because the feature extraction stage differs for each language, it is not scalable. With the introduction of deep learning, handwriting recognition rate improved dramatically.

Challenges in Handwriting Recognition

1. Strokes have a lot of variation and ambiguity from person to person.

2. A user's handwriting style also varies from time to time and is uneven.

3. Degradation of the source text over time has resulted in poor quality.

4. People do not need to write a line of text in a single direction on white paper, yet text in printed documents sits in a straight line.

5. Character separation and recognition are difficult with cursive handwriting.

6. In comparison to printed text, where all of the text sits up straight, the handwritten text might exhibit varied rotation to the right.

7. When compared to synthetic data, acquiring a well-tagged dataset to learn is not affordable.