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20172026
most citedWhat Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

73 citations · 123 across the 14 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV2021★ 3 cited

Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features

Byeonghu Na, Yoonsik Kim, Sungrae Park

Linguistic knowledge has brought great benefits to scene text recognition by providing semantics to refine character sequences. However, since linguistic knowledge has been applied…

cs.CL2021★ 19 cited

BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents

Teakgyu Hong, Donghyun Kim, Mingi Ji +3

Key information extraction (KIE) from document images requires understanding the contextual and spatial semantics of texts in two-dimensional (2D) space. Many recent studies try to…

cs.CV2021

SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models

Moonbin Yim, Yoonsik Kim, Han-Cheol Cho +1

For successful scene text recognition (STR) models, synthetic text image generators have alleviated the lack of annotated text images from the real world. Specifically, they genera…

cs.CV2021★ 1 cited

RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and Styles

Junyeop Lee, Yoonsik Kim, Seonghyeon Kim +4

Scene text editing (STE), which converts a text in a scene image into the desired text while preserving an original style, is a challenging task due to a complex intervention betwe…

cs.LG2021★ 9 cited

Show, Attend and Distill:Knowledge Distillation via Attention-based Feature Matching

Mingi Ji, Byeongho Heo, Sungrae Park

Knowledge distillation extracts general knowledge from a pre-trained teacher network and provides guidance to a target student network. Most studies manually tie intermediate featu…

cs.LG2021★ 4 cited

SWAD: Domain Generalization by Seeking Flat Minima

Junbum Cha, Sanghyuk Chun, Kyungjae Lee +4

Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains. Although a variety of DG methods…