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How can I extract date information from a scanned document or image file using Optical Character Recognition (OCR) technology?
OCR technology is based on machine learning algorithms that learn to recognize patterns in text images, allowing it to extract text from documents and images.
The first OCR engine was developed in the 1960s, but it was not until the 1980s that OCR technology became widely used in commercial applications.
OCR technology uses various pre-processing techniques, such as thresholding, binary inversion, and contour detection, to enhance text recognition.
Tesseract.js, a JavaScript library for OCR, is capable of recognizing text in over 100 languages.
Pytesseract, a Python wrapper for the Tesseract OCR engine, can extract text from images and documents, including scanned PDFs and images.
Regular expressions (regex) are often used to extract specific data, such as dates, from the extracted text.
Optical Character Recognition (OCR) technology can also be used to recognize handwritten text, including dates, from images and documents.
The Sliding Window Algorithm is a technique used to recognize dates from images by segmenting the image into small windows and applying OCR to each window.
iLoveOCR, an online OCR tool, allows users to extract text from images and scanned PDFs and convert them to editable Excel files.
Google Cloud Vision API's OCR feature can detect and extract text from images, including dates, using machine learning algorithms.
Nanonets, an AI-powered OCR platform, allows users to select from pre-trained OCR models or create custom models to extract text and data from images.
Free Online OCR tools like i2OCR can extract text from images and scanned documents, supporting over 100 languages and offering multi-column document analysis.
The accuracy of OCR technology depends on the quality of the scanned image or document, with high-quality images resulting in more accurate text recognition.
OCR technology can also be used to extract data from tables and key-value pairs (KVPs) in images.
Optical Character Recognition (OCR) is often used in conjunction with other computer vision and machine learning techniques, such as object detection and natural language processing (NLP).
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