Papers › Efficient Scene Text Localization and Recognition with Local Character Refinement

Efficient Scene Text Localization and Recognition with Local Character Refinement

14 Apr 2015arXiv:1504.03522archive 2025-07-28

Lukáš Neumann, Jiří Matas

An unconstrained end-to-end text localization and recognition method is presented. The method detects initial text hypothesis in a single pass by an efficient region-based method and subsequently refines the text hypothesis using a more robust local text model, which deviates from the common assumption of region-based methods that all characters are detected as connected components. Additionally, a novel feature based on character stroke area estimation is introduced. The feature is efficiently computed from a region distance map, it is invariant to scaling and rotations and allows to efficiently detect text regions regardless of what portion of text they capture. The method runs in real time and achieves state-of-the-art text localization and recognition results on the ICDAR 2013 Robust Reading dataset.

Paper PDF

Code

No code repository is listed for this paper in the archive or in Syntology's graph.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Scene Text Detection	ICDAR 2013	Neumann et al. *	F-Measure	77.1%	#14 of 16	Archive leaderboard	report
Scene Text Detection	ICDAR 2013	Neumann et al. *	Precision	81.8	#14 of 16	Archive leaderboard	report
Scene Text Detection	ICDAR 2013	Neumann et al. *	Recall	72.4	#14 of 16	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections