TextScanner: Reading Characters in Order for Robust Scene Text Recognition
Table 3: Recognition performance with different settings. “Geo” denotes the geometry branch, “Ord” denotes word formation using the order maps.
['Geo', 'Ord', 'Accuracy (%) IIIT', 'Accuracy (%) SVT', 'Accuracy (%) IC13', 'Accuracy (%) IC15', 'Accuracy (%) SVTP', 'Accuracy (%) CT']
The superiority of TextScanner has been validated via the experiments in Chinese recognition task, here we specifically explore the effect of the geometry branch and the word formation process based on order maps. Note that the word formation with order maps relies on the output of the geometry branch, therefore we use the post-processing procedure of CA-FCN as a replacement. As the order maps ensure the characters are scanned in correct order, the recognition performance is significantly elevated, especially on irregular datasets: 7.4% on IC15 and 10.2% on SVTP. Besides, even with the regular post-processing, the geometry branch still achieves better performance, proving it can facilitate the optimization of the class branch.