OCR (Optical Character Recognition) is a technology that enables the recognition of printed or handwritten text characters within digital images of physical documents, such as scanned documents, photographs, or screenshots. The primary goal of OCR is to convert different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data.
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OCR Introduction
OCR Introduction:
Current Libraries Used:
Tesseract OCR:
Pros:It is one of the most accurate open-source OCR engines available, supporting various languages and document formats. It is highly flexible and can be customized for different applications.
Cons: Tesseract may not perform well with noisy or low-quality images, and it requires preprocessing to enhance the image quality for accurate results.
URL: https://github.com/tesseract-ocr/tesseractGoogle Cloud Vision OCR:
Pros:Offers powerful OCR capabilities with support for multiple languages and advanced image analysis. It provides APIs for easy integration and can handle various image types effectively.
Cons:It requires an internet connection for processing, and there might be privacy concerns due to data being sent to external servers for analysis.
URL: https://cloud.google.com/vision/docs/ocrOCRopus:
Pros:This OCR system is designed for high-performance, and it can handle complex document layouts and formats. It offers extensive pre-processing capabilities and supports multiple languages.
Cons:OCRopus may have a steeper learning curve compared to some other OCR libraries, and it may require significant computational resources for large-scale processing.
URL:https://github.com/tmbdev/ocropyReference Example URLs:
Example of Tesseract OCR implementation in Python:
URL:https://nanonets.com/blog/ocr-with-tesseract/Example of Google Cloud Vision OCR implementation:
Example of Tesseract OCR implementation in Python:
URL:https://cloud.google.com/vision/docs/ocrExample of using OCRopus for OCR tasks:
Example of Tesseract OCR implementation in Python:
URL:https://github.com/tmbdev/ocropy
