OCR OntheGo: Robust EndtoEnd Systems for Reading License Plates & Street Signs We work on the problem of recognizing license plates and street signs automatically in challenging conditions such as chaotic traffic. We leverage stateoftheart text spotters to generate a large amount of noisy labeled training data. The data is filtered using a pattern derived from domain knowledge. We augment training and testing data with interpolated boxes and annotations that makes our training and testing robust. We further use synthetic data during training to increase the coverage of the training data. We train two different models for recognition. Our baseline is a conventional Convolution Neural Network (CNN) encoder followed by a Recurrent Neural Network (RNN) decoder. As our first contribution, we bypass the detection phase by augmenting the baseline with an Attention mechanism in the RNN decoder. Next, we build in the capability of training the model endtoend on scenes containing license plates by incorporating inception based CNN encoder that makes the model robust to multiple scales. We achieve improvements of 3.75% at the sequence level, over the baseline model. We present the first results of using multiheaded attention models on text recognition in images and illustrate the advantages of using multipleheads over a single head. We observe gains as large as 7.18% by incorporating multiheaded attention. We also experiment with multiheaded attention models on French Street Name Signs dataset (FSNS) and a new Indian Street dataset that we release for experiments. We observe that such models with multiple attention masks perform better than the model with singleheaded attention on three different datasets with varying complexities. Our models outperform stateoftheart methods on FSNS and IIITILST Devanagari datasets by 1.1% and 8.19% respectively.

ICDAR 2019 Competition on LargeScale Street View Text with Partial Labeling  RRCLSVT Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavily relies on the amount of fully annotated training data, which is costly and inefficient to obtain. To scale up the amount of training data while keeping the labeling procedure costeffective, this competition introduces a new challenge on Largescale Street View Text with Partial Labeling (LSVT), providing 5,0000 and 400,000 images in full and weak annotations, respectively. This competition aims to explore the abilities of stateoftheart methods to detect and recognize text instances from largescale street view images, closing gaps between research benchmarks and real applications. During the competition period, a total number of 41 teams participate in the two tasks with 132 valid submissions, i.e., text detection and endtoend text spotting. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of ICDAR 2019LSVT challenge.