We examine how machine learning techniques can be used to solve handwriting recognition issues
Recent Deep Learning advancements, such as the introduction of transformer topologies, have helped us accelerate our handwritten character recognition. Intelligent Character Recognition (ICR), is a term used to describe the process for recognizing handwritten content. ICR algorithms require more intelligence than ordinary OCR.
This post will cover the challenges of handwritten text identification and the techniques that can be used to tackle them using deep learning and machine learning.
In the healthcare/pharmaceutical industry, patient medication digitization is a serious issue. Roche processes millions of PDFs each day, processing petabytes in medical PDFs. Handwritten character recognition is also important in areas such as patient enrollment and form digitization. Handwriting analysis can be a great service for hospitals and pharmaceutical companies.
An insurance company that is large receives more than 20 million documents each day. A delay in processing a claim can have a serious impact on the business. There may be a variety in the claims documents, so relying on only human processing can slow down the process.Also read: 14 Best Webinar Software Tools in 2021 (Ultimate Guide for Free)
Regularly, people write checks. They continue to play an important role in most non-cash transactions. In many developing countries, the current check processing method involves staff at a bank to manually enter the information on cheques and verify data like signature and date. A handwriting textual recognition system is a cost-saving tool that can be used to save time and money on bank processing.
Images of historic knowledge are being digitalized and made accessible to the public by uploading scans. This effort will not be effective if the text within the photos cannot be identified and indexed, queried, and browsed. Handwriting identification is crucial for bringing twentieth-century papers, postcards and research works alive.
In the initial attempts to recognize handwriting, ML methods like Hidden Markov Models (HMM), SVM, and others were used. After pre-processing the text, feature extraction is used for identifying key information about each character. This includes loops, tipping points, aspect ratios, and so forth. These features are then passed to a classifier like HMM to get the results. The machine-learning algorithms‘ performance can be limited by the manual feature extraction and their low learning capacity. It is not scaleable because the feature extraction stage for each language is different. The handwriting recognition rate has increased dramatically since the introduction of deep learning.
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