Abstract: Systems and methods for optical character recognition using specialized confidence functions. An example method comprises: receiving a grapheme image; computing a feature vector representing the grapheme image in a space of image features; and computing a confidence vector associated with the grapheme image, wherein each element of the confidence vector reflects a distance, in the space of image features, between the feature vector and a center of a class of a set of classes.

Abstract: Embodiments of the present disclosure describe a system and method for optical character recognition. In one embodiment, a system receives an image depicting text. The system extracts features from the image using a feature extractor. The system applies a first decoder to the features to generate a first intermediary output. The system applies a second decoder to the features to generate a second intermediary output, wherein the feature extractor is common to the first decoder and the second decoder. The system determines a first quality metric value for the first intermediary output and a second quality metric value for the second intermediary output based on a language model. Responsive to determining that the first quality metric value is greater than the second quality metric value, the system selects the first intermediary output to represent the text.

Abstract: Techniques are disclosed for creating event sequences from event data and then providing a visual analysis of event sequences. An event sequencing application analyzes event-related data in order to group events in accordance with predetermined grouping criteria and to sort the events in a chronological order to generate the event sequences. The event sequencing application further provides calculated sequence-specific metrics and a visual representation of event sequences for an event set, thus allowing a user to sort, filter, query, and perform various other types of analysis over the event sequences.