TL;DR
A Novel Coot-based Deep Residual Model (CbDRM) designed for robust scene text recognition, which achieves 97.9% recognition accuracy, outperforming state-of-the-art approaches and exhibiting a substantially lower error rate.
The widespread applications of scene text detection have propelled it into the spotlight as a prominent area of research. However, scene text detection presents a formidable challenge in real-world scenarios, given the complexities arising from diverse backgrounds, text styles, irregular arrangements, and image blurrin...
To solve the problems faced by Chinese character recognition, such as complex shapes and diverse structures, this paper adopts the Visual Geometry Group 16 (VGG-16) model for feature extraction and introduces a two-layer bidirectional Long Short-Term Memory (LSTM) network. It improves the Faster Region-based Convolutio...
Script identification remains challenging for several applications, including handwritten text recognition, scene text analysis, and low-resource languages such as Kashmiri, for which benchmark datasets for standardization are limited. The study addresses this challenge by collecting a multilingual dataset of Kashmiri,...
Study of explicit character-level guidance for scene text detection and recognition indicates that character semantics for detection and local visual evidence for decoding are an effective way to improve robustness in difficult scene text.
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