method: CLOVA-AI v22019-02-18

Authors: Jeonghun Baek, Junyeop Lee, Sungrae Park, Moonbin Yim, Seonghyeon Kim, Hwalsuk Lee

Description: We used Thin-plate-spline (TPS) based Spatial transformer network (STN) which normalizes the input text images, ResNet based feature extractor, BiLSTM, and attention mechanism.
This model was developed based on the analysis of scene text recognition modules.
See our paper and source code.

method: PIONEER2013-04-06

Authors: Jerod Weinman

Description: The Grinnell PIONEER system (Probabilistic Integrator of Noisy Evidence Enabling Reading) of Weinman et al. first segments each word image into superpixels with a mixture of position-conditional regressors. For binarization, a logistic regression classifier then chooses the mixture component(s) most likely to be text. We robustly fit second degree polynomials to the tops and bottoms of binarized characters with an EM algorithm. Word images are then normalized to a horizontal, linear orientation with a thin-plate spline. Finally, a discriminative semi-Markov model jointly segments and recognizes characters in the normalized image using steerable pyramid features, character bigrams, and a large lexicon.

Ranking Table

Description Paper Source Code
DateMethodTotal Edit distance (case sensitive)Correctly Recognised Words (case sensitive)T.E.D. (case insensitive)C.R.W. (case insensitive)
2019-02-18CLOVA-AI v226.951595.98%23.483696.35%

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Ranking Graphic