58 citations · 65 across the 5 of their papers we have counts for
8 papers
The Grind for Good Data: Understanding ML Practitioners' Struggles and Aspirations in Making Good Data
Inha Cha, Juhyun Oh, Cheul Young Park +2
We thought data to be simply given, but reality tells otherwise; it is costly, situation-dependent, and muddled with dilemmas, constantly requiring human intervention. The ML commu…
Character Region Attention For Text Spotting
Youngmin Baek, Seung Shin, Jeonghun Baek +4
A scene text spotter is composed of text detection and recognition modules. Many studies have been conducted to unify these modules into an end-to-end trainable model to achieve be…
CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks
Youngmin Baek, Daehyun Nam, Sungrae Park +5
Despite the recent success of text detection and recognition methods, existing evaluation metrics fail to provide a fair and reliable comparison among those methods. In addition, t…
Few-shot Compositional Font Generation with Dual Memory
Junbum Cha, Sanghyuk Chun, Gayoung Lee +3
Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Despite the remarkable success of existing font generation methods, they have…
On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention
Junyeop Lee, Sungrae Park, Jeonghun Baek +3
Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods still fail to re…
TedEval: A Fair Evaluation Metric for Scene Text Detectors
Chae Young Lee, Youngmin Baek, Hwalsuk Lee
Despite the recent success of scene text detection methods, common evaluation metrics fail to provide a fair and reliable comparison among detectors. They have obvious drawbacks in…