The Rise of AI-based OCR Systems

OCR (Optical Character Recognition) technology has been around for decades. Traditionally, it used algorithms to identify and extract text from images.

• Earliest examples are Emanuel Goldberg’s machine in 1914 that could read characters and convert them into standard telegraph code. In the late 1920s, he developed a more sophisticated machine that could read text printed in different fonts and sizes.
• In the 1950s, OCR technology began to be used in commercial applications. American Machine and Foundry Company (AMF) in 1954 developed a system was used to read zip codes on mail.
• In 1966, IBM developed Magnetic Ink Character Recognition (MICR) system - still used today to read the numbers on checks.
• In 1974, Ray Kurzweil developed a reading machine for blind people that used OCR technology.
• 1980s saw a surge in PCs and affordable OCR technology, which led to a surge in the use of OCR in businesses and government agencies - to read invoices, contracts, and other documents.
• In 1990s OCR systems became more accurate and they could read a wider range of fonts and languages.

While algorithm-based OCR systems peaked in early 2000s, another trend became mainstream – AI-based OCR. The history of AI-based OCR systems can be traced back to the early 1970s, when researchers began to explore the use of artificial intelligence (AI) to improve the accuracy and versatility of OCR systems.

One of the earliest AI-based OCR systems was developed by IBM in 1974. This system used a technique called "template matching" to compare the scanned image of a document to a database of pre-existing templates. If the scanned image matched a template, the system would be able to identify the text in the document with high accuracy.

However, template matching systems were limited in their ability to recognize text in documents with complex layouts or unusual fonts. In the 1980s, researchers began to develop more sophisticated AI-based OCR systems that used techniques such as "neural networks" and "hidden Markov models" to improve accuracy and versatility.

#NeuralNetworks are a type of AI that is inspired by the human brain. They are able to learn from data and make predictions based on that data. Hidden Markov models are a type of statistical model that can be used to predict the probability of a sequence of events.

AI-based OCR systems that use neural networks and hidden Markov models are able to recognize text in documents with complex layouts and unusual fonts with high accuracy. They are also able to read text in a wider range of languages than traditional OCR systems.

In the 2000s, AI-based OCR systems became even more advanced. They were able to read text from images with poor quality or low resolution. They were also able to extract more information from documents, such as tables, forms, and images.

AI-based OCR systems are now widely used in a variety of applications, such as document management, invoice processing, and healthcare. They are also used to digitize books and other historical documents.

Here are some of the key milestones in the history of AI-based OCR systems:

• 1974: IBM develops the first AI-based OCR system that uses template matching.
• 1980s: Researchers develop AI-based OCR systems that use neural networks and hidden Markov models.
• 1990s: AI-based OCR systems become more accurate and versatile.
• 2000s: AI-based OCR systems become even more advanced and are used in a variety of applications.
• 2020s: New AI-based OCR systems are being developed that are even more accurate and versatile.

Google Cloud's Document AI is an example of a more advanced OCR solution that overcomes many of the limitations of traditional OCR. Document AI uses #MachineLearning to extract text from images with high accuracy, even in images with poor quality or complex layouts. Document AI also supports a wide range of languages, making it a good choice for businesses that need to process documents in multiple languages.

Here are some of the key differences between traditional OCR and Google Cloud's Document AI:

• Accuracy:?Document AI is more accurate than traditional OCR, even in images with poor quality or complex layouts.
• Support for languages:?Document AI supports a wider range of languages than traditional OCR.
• Features:?Document AI can extract more information from documents than traditional OCR, such as tables, forms, and images.
• Scalability:?Document AI is more scalable than traditional OCR, making it a good choice for businesses that need to process large volumes of documents.

Overall, Google Cloud's Document AI is a more powerful and versatile OCR solution than traditional OCR. If you need to extract text from images with high accuracy and support for a wide range of languages, then Document AI is a good choice for you.

Here are some additional resources that you may find helpful:

• Google Cloud Document AI documentation: https://cloud.google.com/document-ai/docs/
• Google Cloud Document AI blog: https://cloud.google.com/blog/products/ai-machine-learning/top-reasons-to-use-gcp-document-ai-ocr
• Comparison of OCR solutions: https://www.capterra.com/ocr-software/compare/

For collaborating and sharing best practices on how to harness Google Cloud's industry leading AI portfolio, reach out to our practice leaders at Intelliswift - An LTTS Company - Vadivel Devarajan , Arvind Sampath , Naveen Totla , Supriya Rao , Sekhar Annambhotla . We are also at #GoogleCloudNext2023 and are looking forward to seeing you there.