J
Joan Mas
Researcher at Autonomous University of Barcelona
Publications - 24
Citations - 1521
Joan Mas is an academic researcher from Autonomous University of Barcelona. The author has contributed to research in topics: Parsing & Adjacency list. The author has an hindex of 10, co-authored 24 publications receiving 1207 citations.
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Proceedings ArticleDOI
ICDAR 2013 Robust Reading Competition
Dimosthenis Karatzas,Faisal Shafait,Seiichi Uchida,Masakazu Iwamura,Lluís Gómez i Bigorda,Sergi Robles Mestre,Joan Mas,David Fernandez Mota,Jon Almazan,Lluís-Pere de las Heras +9 more
TL;DR: The datasets and ground truth specification are described, the performance evaluation protocols used are details, and the final results are presented along with a brief summary of the participating methods.
Proceedings ArticleDOI
ICDAR 2011 Robust Reading Competition - Challenge 1: Reading Text in Born-Digital Images (Web and Email)
TL;DR: This paper presents the results of the first Challenge of ICDAR 2011 Robust Reading Competition, focused on the extraction of text from born-digital images, specifically from images found in Web pages and emails.
Proceedings ArticleDOI
An Incremental On-line Parsing Algorithm for Recognizing Sketching Diagrams
TL;DR: The proposed method consists in an incremental on-line predictive parser based on symbol descriptions by an adjacency grammar that analyzes input strokes as they are drawn by the user and is able to get ahead which symbols are likely to be recognized when a partial subshape is drawn in an intermediate state.
Proceedings ArticleDOI
End-to-End Handwritten Text Detection and Transcription in Full Pages
TL;DR: The experimental results show that the proposed end-to-end framework to transcribe full pages can achieve comparable results to models that assume segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two task separately.
Book ChapterDOI
A Platform to Extract Knowledge from Graphic Documents. Application to an Architectural Sketch Understanding Scenario
TL;DR: A general architecture to extract knowledge from graphic documents that consists of a set of modules able to extract descriptors that, combined with domain-dependent knowledge and recognition strategies, allow to interpret a given graphical document.