Abstract: Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and/or some other method appropriate for rectifying images received.

Abstract: In some implementations, a data quality system may obtain a historical dataset that includes historical values for a data element. The data quality system may generate one or more statistical summaries for the data element based on the historical values for the data element. The data quality system may generate, using a statistical model, a confidence interval defined by an upper threshold and a lower threshold based on the one or more statistical summaries, wherein the upper threshold and the lower threshold define a predicted range for a current value for the data element. The data quality system may receive a current dataset that includes the current value for the data element. The data quality system may generate an output that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold.