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MhLiao DB - GitHub This is a PyToch implementation of DBNet and DBNet++(TPAMI, arxiv) It presents a real-time arbitrary-shape scene text detector, achieving the state-of-the-art performance on standard benchmarks Part of the code is inherited from MegReader
DBNet - Nithish Duvvuru - GitHub Pages DBNet Introduction Differentiable Binarization is an architecture used for text detection, particularly in the context of scene text recognition It aims to address the challenges of accurately segmenting text regions from complex backgrounds in images
DBNet Text Detector - Wolfram Neural Net Repository DBNet Text Detector Trained on ICDAR-2015 and Total-Text Data Detect and localize text in an image Released in 2022, this family of segmentation networks introduces a novel framework for detecting arbitrary-shape scene text
CC-DBNet: A Scene Text Detector Combining Collaborative . . . - Springer To address these issues, we propose a scene text detector called CC-DBNet This detector combines Intra-Instance Collaborative Learning (IICL) and the Cascaded Feature Fusion Module (CFFM) to detect arbitrary-shaped text instances
[22. 02] DBNet++ | DOCSAID Following up on the previous DBNet paper, the authors recognized room for further improvement in the model They proposed DBNet++, which builds upon the existing architecture by optimizing the way features are fused
Text Detection Models — MMOCR 1. 0. 1 documentation - Read the Docs In this paper, we propose a Differentiable Binarization (DB) module that integrates the binarization process, one of the most important steps in the post-processing procedure, into a segmentation network