License Plate Character Segmentation Using Key Character Location and Projection Analysis Character segmentation forms a link between license plate detection and character recognition. This paper presents a twostage character segmentation framework. Firstly, a cascade classifier is trained by AdaBoost algorithm to locate key character which represents an administrative area. Then, based on key character location information, the rest of characters can be predicted or estimated. For the neighbor character of key character, it can be predicted by connected components analysis. For other characters that are far from the key character, a vertical projection strategy without hyphens is proposed to segment them. This strategy is promising to address the issue of touching characters. To illustrate the effectiveness of character segmentation, broad learning system is adopted to classify segmented characters. Experimental results performed on Macau license plates demonstrate the proposed methodxe2x80x99s effectiveness in comparison with some stateoftheart approaches. It is expected to apply the designed techniques to license plate character segmentation of other regions or countries that have similar situations with Macauxe2x80x99s.

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.