Scene Text Detection and Recognition

Abstract

Deep learning methods have significantly reduced the difficulties related to multi-oriented text detection in recent scene text detection advances. The restrictions of conventional text representations, like horizontal boxes, rotated rectangles, or quadrangles, make it difficult to recognize curved writing. In order to tackle this problem, we provide a novel approach that uses instance-aware segmentation to identify irregular scene texts. Our method presents a semantic segmentation model that is led by attention and is intended to accurately label the weighted borders of text areas. Tests on multiple popular benchmarks show that, In contrast to cutting-edge techniques, our methodology delivers better performance on curved text datasets and maintains comparable results on multi-oriented text datasets. Simultaneously, despite encouraging results in scene text detection, the complexity of the multi-stage pipelines used by present approaches sometimes causes them to fail in difficult settings. We offer a strong and simplified pipeline that uses a single neural network to predict words or text lines of variable quadrilateral forms and orientations in complete images, removing the need for needless intermediate steps. This simplicity makes it possible to concentrate on creating neural network designs and loss functions. Our examinations using reference datasets reveal that our suggested approach performs substantially superior to the majority advanced methods concerning precision and efficiency.