Leverton Holding, LLC

Florian Kuhlmann

Michael Kieweg

Christian Schäfer

Saurabh Shekhar Verma

Systems and methods for transforming data between multiple styles are provided. In one embodiment, a system is provided that includes a generator model, a discriminator model, and a preserver model. The generator model may be configured to receive data in a first style and generate converted data in a second style. The discriminator model may be configured to receive the converted data from the generator model, compare the converted data to original data in the second style, and compute a resemblance measure based on the comparison. The preserver model may be configured to receive the converted data from the generator model and compute an information measure of the converted data. The generator model may also be trained to optimize the resemblance measure and the information measure.

Data style transformation with adversarial models

A method for splitting text line images includes receiving a text line image and identifying that the text line image comprises a plurality of zones, wherein each zone includes text whose font differs from the text of adjacent zones. The method further includes selecting a splitting position between multiple zones and splitting the text line image at the splitting position into a plurality of image segments, wherein each image segment contains at least one zone of the text line image and performing optical character recognition on each image segment to recognize a text segment of the image segment. In certain implementations, the method further includes generating one or more confidence measurements and selecting a splitting position that corresponds to a large gradient in the confidence measurement.

Text line image splitting with different font sizes

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.

Text line normalization systems and methods

A method for identifying errors associated with named entity recognition includes recognizing a candidate named entity within a text and extracting a chunk from the text containing the candidate named entity. The method further includes creating a feature vector associated with the chunk and analyzing the feature vector for an indication of an error associated with the candidate named entity. The method also includes correcting the error associated with the candidate named entity.

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Berlin, Germany

Florian Kuhlmann