Deep Learning-Based Cursive Text Detection and Recognition in Natural Scene Images

Abstract

Cursive text detection and recognition in natural scene images are complex tasks due to the variability and intricacy of handwriting styles. This research focuses on developing a deep learning-based approach to address these challenges. The proposed solution leverages advancements in deep learning techniques to improve the accuracy and robustness of cursive text detection and recognition. The research involves collecting and annotating a diverse dataset of natural scene images containing cursive text. A deep learning model for cursive text detection is trained on the annotated dataset, and techniques for text line segmentation are investigated to enhance accuracy. Furthermore, a deep learning-based recognition system is designed and implemented to transcribe cursive text into machine-readable text. The proposed approach is evaluated and compared with existing methods using appropriate metrics and benchmark datasets. The research aims to provide insights into the challenges and opportunities of cursive text analysis in real-world scenarios and contribute to advancements in document digitization, handwriting analysis, and information retrieval.