What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

412 indexed citations
published 2019

Countries where authors are citing What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

Specialization
Citations

This map shows the geographic impact of What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis more than expected).

Fields of papers citing What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis.

About What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

This paper, published in 2019, received 412 indexed citations . Written by Jeonghun Baek, Geewook Kim, Junyeop Lee, Sungrae Park, Dongyoon Han, Sangdoo Yun, Seong Joon Oh and Hwalsuk Lee covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (362 citations), Artificial Intelligence (124 citations), Media Technology (100 citations), Information Systems (15 citations) and Industrial and Manufacturing Engineering (12 citations).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1109/iccv.2019.00481.

Explore hit-papers with similar magnitude of impact