58 citations · 69 across the 6 of their papers we have counts for
7 papers
CREPE: Coordinate-Aware End-to-End Document Parser
Yamato Okamoto, Youngmin Baek, Geewook Kim +5
In this study, we formulate an OCR-free sequence generation model for visual document understanding (VDU). Our model not only parses text from document images but also extracts the…
TRACE: Table Reconstruction Aligned to Corner and Edges
Youngmin Baek, Daehyun Nam, Jaeheung Surh +2
A table is an object that captures structured and informative content within a document, and recognizing a table in an image is challenging due to the complexity and variety of tab…
DEER: Detection-agnostic End-to-End Recognizer for Scene Text Spotting
Seonghyeon Kim, Seung Shin, Yoonsik Kim +6
Recent end-to-end scene text spotters have achieved great improvement in recognizing arbitrary-shaped text instances. Common approaches for text spotting use region of interest poo…
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…
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…
Character Region Awareness for Text Detection
Youngmin Baek, Bado Lee, Dongyoon Han +2
Scene text detection methods based on neural networks have emerged recently and have shown promising results. Previous methods trained with rigid word-level bounding boxes exhibit…