Abstract: A method for estimating text heights of text line images includes estimating a text height with a sequence recognizer. The method further includes normalizing a vertical dimension and/or position of text within a text line image based on the text height. The method may also further include calculating a feature of the text line image. In some examples, the sequence recognizer estimates the text height with a machine learning model.

Abstract: Novel one vs. all based extensions to Generative Adversarial Networks (GANs) are disclosed, which can be applied to multiclass classification problems with changing classes in a distributed setting. GANs can be used in semi-supervised classification by providing the class label information to discriminator from real training data. Instead of using the discriminator as a label classifier, a separate network component or module—referred to as head discriminator—is appended which labels the input instances created by the generator. The discriminator is kept as a binary classifier (as in existing GANs) which only differentiates between true data and the output of the generator. The newly added head discriminator learns to discriminate between one vs all class from the generator's output.

Abstract: A method for estimating text heights of text line images includes estimating a text height with a sequence recognizer. The method further includes normalizing a vertical dimension and/or position of text within a text line image based on the text height. The method may also further include calculating a feature of the text line image. In some examples, the sequence recognizer estimates the text height with a machine learning model.

Abstract: A method for estimating text heights of text line images includes estimating a text height with a sequence recognizer. The method further includes normalizing a vertical dimension and/or position of text within a text line image based on the text height. The method may also further include calculating a feature of the text line image. In some examples, the sequence recognizer estimates the text height with a machine learning model.

Abstract: A method for estimating text heights of text line images includes estimating a text height with a sequence recognizer. The method further includes normalizing a vertical dimension and/or position of text within a text line image based on the text height. The method may also further include calculating a feature of the text line image. In some examples, the sequence recognizer estimates the text height with a machine learning model.

Abstract: Methods and systems are described that determine the promotion(s) offering the best monetary discount available to a customer. The methods and systems can be used at the time of checkout during a retail purchase or prior to checkout to inform the customer about the best deal that may be available based on alternative proposed purchases. One method can include defining a promotion combinability matrix that identifies combination logic of the available promotions. In addition, a priority set of the available promotions is established that identifies the priority in which the promotions will be applied. The promotion combinability matrix and the priority set are then used to determine the best monetary discount that is available to the customer.