Extract text from images using OCR
Identify lottery numbers from images
Extract text from documents
NepaliOCR
OCR System. Homepage: https://github.com/Topdu/OpenOCR
Scan and extract text from documents
Convert images of text into digital text
Convert images of text into editable text
Convert scanned images to text
Extract text from images
Extract text from handwritten images
Extract text from images using OCR
Convert images to text
OCR Using GOT And Tesseract is a powerful tool designed to extract text from images using Optical Character Recognition (OCR) technology. It combines the capabilities of two advanced technologies: GOT (a pre-trained model for text detection) and Tesseract (an open-source OCR engine). This combination enables highly accurate text extraction from various types of document images.
• Text Extraction from Images: Extract text from scanned documents, photos, and other image formats. • Multi-Language Support: Recognize and extract text in multiple languages thanks to Tesseract's robust language capabilities. • High Accuracy: GOT's text detection technology ensures that text is precisely identified and localized within images. • Flexibility: Works with various image formats and resolutions, making it suitable for a wide range of OCR applications. • Open-Source: Built on Tesseract, a widely-used and trusted OCR engine, ensuring customizability and community support.
What languages does OCR Using GOT And Tesseract support?
OCR Using GOT And Tesseract supports a wide range of languages, including English, Spanish, French, German, Italian, Chinese, Japanese, and many others, thanks to Tesseract's extensive language support.
Can this tool work with non-Latin scripts?
Yes, Tesseract supports many non-Latin scripts, including Arabic, Hebrew, Russian, and others, making this tool versatile for global use cases.
Why is the combination of GOT and Tesseract better than using Tesseract alone?
The integration of GOT's text detection capabilities ensures that text is accurately identified and localized within images, which improves the overall accuracy of OCR results compared to using Tesseract alone.