Patch Aggregator for Scene Text Script Identification Script identification in the wild is of great importance in a multilingual robustreading system. The scripts deriving from the same language family share a large set of characters, which makes script identification a finegrained classification problem. Most existing methods make efforts to learn a single representation that combines the local features by making a weighted average or other clustering methods, which may reduce the discriminatory power of some important parts in each script for the interference of redundant features. In this paper, we present a novel module named Patch Aggregator (PA), which learns a more discriminative representation for script identification by taking into account the prediction scores of local patches. Specifically, we design a CNNbased method consisting of a standard CNN classifier and a PA module. Experiments demonstrate that the proposed PA module brings significant performance improvements over the baseline CNN model, achieving the stateoftheart results on three benchmark datasets for script identification: SIW13, CVSI 2015 and RRCMLT 2017.

Script Identification using Across and WithinImage Distribution Estimation In this paper, we apply several modifications to script identification, several of which inspired by techniques from the similar audio task of spoken language recognition. Specifically, we alter the architecture of a convolutional network with global average pooling to include variance pooling as well, we utilize score calibration of the output scores of the network, and we utilize prior distribution estimation to condition the calibrated scores. We show that these methods are effective in script identification, with the use of priors showing especially promising improvements. Furthermore, in the domain of script identification, several additional extensions of distribution estimation are available which consider the distribution within each image, and we demonstrate much larger improvements when employing these extensions. Finally, we also show that an embeddingplusclassifier approach performs similarly to the full network, and so its potential for increased flexibility may be beneficial for future consideration. With all modifications, overall accuracy on the ICDAR 2017 validation dataset increases from 89.7% to 93.6%.