Vehicle and plate detection web application with Yolov5, EasyOCR and Flask.

YOLOv5 model trained on a custom dataset to detect car plates. Detected plates are passed to EasyOCR to read them. Implemented simple object tracking to improve accuracy.

Extracting Business Card With OCR

This Project is to develop a Streamlit application that enables users to upload a business card image and extract essential information from it using easyOCR. The extracted information includes the company name, card holder namebjective of, designation, mobile number, email address, website URL, area, city, state, and pin code. #GUI

Extracting Business Card Data with EasyOCR

Using EasyOCR for bangla and english both license plate detection

License Plate Recognition using YOLOv8 + EasyOCR + NAFNet

This is an AI, which will detect the number plate of a car from a given image and extract the Number Plate data from that Image.

An Automatic License Plate Detection model which tracks cars and OCR's their number plates to detect the text. Also, the recognised text is written to the video.

This Project is a part of Computer Vision

With the EasyOCR library, the software can recognize up to 80 languages. Using the OpenCV library, we can recognize text in realtime.

This repo focuses on my works in the field of Computer Vision

Python script that sorts image files based on the date found in the image's OCR (Optical Character Recognition) result using the EasyOCR and dateparser libraries.

TFG

Number Plate Detection using OpenCV

ML assignment for Research papers into Audiobook using Google Vision API & Google Text to Speech AI integration to Web app

This is a simple mortgage repayment admin app built with Streamlit. It is a frontend app that allow a receipt to be uploaded and apply Optical Character Recognition (OCR) to extract information from the image.

Computer Vision və OCR vasitəsilə, şəkildəki avtomobil nömrəsinin oxunulması

OCR_app can convert text from picture to written text. It's a full-stack machine-learning application that uses Hugging Face, Streamlit and EasyOcr .