Object recognition or Detection is the technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades.

Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).

PROJECT REQUIREMENTS
Implement an object detector which identifies the classes of the objects in
an image or video. OR
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Character detector which extracts printed or handwritten text from an
image or video.
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Below resources are just for references you can use any library/approach
to achieve the goal.
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Resources: link1 link2
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Project submission:
1. Host the code on GitHub Repository (public). Record the code and
output in a video. Post the video on YouTube
2. Share links of code (GitHub) and video (YouTube) as a post on YOUR
LinkedIn profile
3. Submit the LinkedIn link in Task Submission Form when shared with
you.
4. Please read FAQs on how to submit the tasks.

GIT HUB CODE LINK
[ Ссылка ]

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