Image Detection and Data extraction Using Hybrid Deep Learning Techniques

In the current age of data, numerous pictures are everywhere that permit the extraction of text and certain image-based information. There are several technologies/ tools that can be used to accomplish this task. Optical Character Recognition (OCR) is a vital technology to automate text extraction from pictures, specifically for identifying people through Identification cards. This paper introduces a hybrid system that integrates few conventional OCR utilities such as PyTesseract with deep learning algorithms, including Mask Region-based Convolutional Neural Networks (R-CNN) for object detection and Convolutional Recurrent Neural Network (CRNN) for text recognition. The system also boosts the text extraction with the application of sophisticated preprocessing techniques such as noise removal, binarization, and edge detection, which enhance image quality and recognition accuracy. After the text is extracted, the text extracted is well-arranged and stored in an Excel file to make it convenient to store and retrieve. The system is compared with the general traditional OCR systems, and that the system demonstrates improvements in accuracy rate, speed of processing, and error correction, Also even under difficult conditions such as low-resolution images and varying lighting. The suggested system is ideal for verification of identities in the majority of the sectors like banking, government, and education. The future developments will involve support for multi-languages and compatibility with mobile devices so that the system becomes even more efficient and versatile with user-friendly.