Optical Character Recognition (OCR) is a technology that has been around for decades, used primarily for digitizing printed text. With the advent of Machine Learning, OCR has become increasingly sophisticated, allowing it to recognize not just printed text, but also handwriting and even images. In this article, we will explore the capabilities of OCR with Machine Learning, focusing on Tesseract, an open-source OCR engine, and other modern OCR technologies that go beyond its limitations.
Tesseract OCR
Tesseract is an open-source OCR engine that has been around since the 1980s. Originally developed by HP, it was later released under an Apache 2.0 license and is now maintained by Google. Tesseract is capable of recognizing printed text in over 100 languages and is highly customizable, with the ability to add new fonts, character sets, and languages. It also has a simple API, making it easy to use in applications. However, Tesseract does have some limitations. It struggles with recognizing text in images that are low-resolution or have poor contrast, and it is not well-suited for recognizing handwriting.
Beyond Tesseract
While Tesseract is a powerful OCR engine, there are other modern OCR technologies that go beyond its capabilities. For example, Amazon Textract is a cloud-based OCR service that can recognize both printed and handwritten text, as well as tables and forms. It uses Machine Learning to improve its accuracy over time and can be integrated with other AWS services. Google Cloud Vision API is another cloud-based OCR service that can recognize printed and handwritten text, as well as images and logos. It also has advanced features such as object detection and facial recognition.
Advancements in OCR
The latest advancements in OCR technology have made it possible to recognize text in more challenging environments. For example, Microsoft’s Cognitive Services OCR can recognize text in videos, making it useful for transcription or subtitling. Another advancement is the use of Deep Learning in OCR, which has led to significant improvements in accuracy. Facebook’s OCR system, Rosetta, uses Deep Learning to recognize text in images and videos in over 100 languages. These advancements have opened up new possibilities for OCR in various industries, from healthcare to finance to retail.
OCR in Healthcare
OCR technology has many applications in healthcare, from digitizing patient records to interpreting medical images. For example, Zebra Medical Vision uses OCR to extract data from medical images and automatically classify them. This allows doctors to quickly identify patients who need urgent attention and prioritize their care. Another use case is the recognition of handwritten prescriptions, which can be difficult for human pharmacists to read accurately. With OCR, these prescriptions can be digitized and analyzed for errors.
Conclusion
OCR with Machine Learning has come a long way in recent years, with advancements in accuracy, speed, and versatility. While Tesseract is still a popular OCR engine, there are other modern OCR technologies that go beyond its capabilities, such as Amazon Textract, Google Cloud Vision API, and Microsoft’s Cognitive Services OCR. These advancements have opened up new possibilities for OCR in various industries, from healthcare to finance to retail, and are only expected to improve with further development.