method: StradVision2015-03-23

Authors: Hojin Cho et al.

Description: First, we extract character candidates using extremal regions (ER) Second, we verify the extracted character candidates with the character classifier trained by Agile Learning. Afterwards, we do text-patch matching which greatly enhances the recall rate, and group the characters into text regions. Finally, we apply a deep neural network for character recognition.

method: BUCT_YST2015-01-12

Authors: Wei Hu

Description: Multiple MSERs and Convolution Neural Networks are employed in our method to extract character candidates. Character candidates are then grouped by using the technique in USTB_TexStar to generate text candidates. At last, a text classifier is trained on the training dataset to verify these text candidates. The related demo of the software has been released on http://research.cs.buct.edu.cn/huwei ( or http://124.205.208.198:8081).

method: I2R_NUS_FAR2013-04-09

Authors: Lu Shijian, Tian Shangxuan, Lim Joo Hwee, Tan Chew Lim

Description: This method builds upon I2R_NUS by further reducing false alarms through a machine learning approach.

Ranking Table

Description Paper Source Code
DateMethodPx. RecallPx. PrecisionPx. F-scoreWell s.MergedBrokenBr.-Mer.LostFalse p.DetectedRecallPrecisionFscore
2015-03-23StradVision78.80%89.24%83.70%4417316120112384507675.27%86.96%80.69%
2015-01-12BUCT_YST74.56%81.75%77.99%42583093451262201499872.56%84.37%78.02%
2013-04-09I2R_NUS_FAR74.73%81.70%78.06%4080317811462355499869.53%81.49%75.04%
2013-04-08NSTextractor60.71%76.28%67.61%3757108701996345445064.03%84.36%72.80%
2013-04-07USTB_FuStar69.58%74.45%71.93%40913111801448966550969.72%74.12%71.85%
2013-04-08I2R_NUS73.57%79.04%76.21%3590743701528357462061.18%77.66%68.44%
2013-04-08NSTsegmentator68.41%63.95%66.10%404615618016482792734168.95%54.98%61.18%
2013-04-06Text Detection64.74%76.20%70.01%372433925417761885633863.46%57.97%60.59%

Ranking Graphic

Ranking Graphic