Extraction of Text from Images Using Deep Learning
M. Sinthuja, Chirag Ganesh Padubidri, Gaddam Sai Jayachandra, Mudduluru Charan Teja, Golthi Sai Pavan Kumar · Procedia Computer Science · 2024
In the recent scenario, it is important to extract the text from various formats, including handwritten and documents. The ability to accurately detect and recognize text, is a crucial task in many fields, such as OCR systems, document analysis, and image processing. Convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are used in a unique method for text detection. Additionally, CNN consist of six sequential layers by using Adam optimizer and SoftMax. The proposed approach is designed effectively to handle the variability and complexity of text in both handwritten and document formats. The proposed approach achieved a significant improvement in text detection and recognition accuracy, with 88.5 percentage while comparing with the existing techniques of CNN and LSTM which has an accuracy of 81.8 percentage.