Older versions of Tesseract used a combination of image processing and statistical models, but the latest versions use deep learning algorithms. It consists of the tesseract-ocr engine and language-specific wrappers like pytesseract for Python. Python Tesseract-ocr recognition on a legal document - missed words, spelling mistakes, and handwritten text ignored ( Source )
In contrast, dense text refers to text in images where text is the primary content and the focus, such as text in books, invoices, and documents. Scene text refers to text that's incidentally present in a photo, such as text on product labels, billboards, traffic signs, vehicles, and so on.
Text extraction often refers to the overall question of how to extract text using all three subtasks - detection, recognition, and information extraction. Information extraction refers to understanding the semantics and purpose of a piece of text. Text recognition refers to recognizing higher-level entities like characters, words, sentences, paragraphs, language, and other concepts of text organization using any kind of real-world knowledge such as language models and document layouts. Optical character recognition (OCR) refers to identifying characters using only the pixels in an image. Text detection refers to estimating which pixels in an image belong to text content. Let's start exploring how we have implemented our text extraction pipeline, starting with some basic concepts you should know for a foundational understanding. For new customer data, we just need a few dozen documents - regardless of file format - to fine-tune our system and have it produce accurate results. That's because our system can generalize well but, at the same time, is also flexible and customizable. We use the same text extraction system for all three use cases, though they seem so different. Our system can accurately extract text information from medical records, patient forms, prescriptions, handwritten opinions, medical imagery, and more. Medical Document Transcription & AutomationĪccurate transcription of medical documents is necessary to deliver high quality of healthcare, avoid legal liabilities, and resolve insurance problems smoothly. They can capture and extract product labels, bar codes, and other information that's critical for both back-office and storefront management in the retail and e-commerce industry. We have automated warehouse workflows and improved storefront operations by deploying our text extraction system for our retail and e-commerce customers.