Abstract: In some embodiments, apparatuses and methods are provided herein useful to train a machine learning algorithm to detect text of interest. In some embodiments, there is provided a system to detect vertically oriented text of interest including a first data set comprising a plurality of captured digital images each depicting an object of interest and a second data set comprising a plurality of augmented digital images each depicting a captured digital image augmented with a synthetic text image; a first control circuit configured to cause the machine learning algorithm to output a machine learning model trained to automatically detect occurrences of vertically oriented text of interest based on the first data set and the second data set; at least one camera; and a second control circuit configured to execute the machine learning model to automatically detect vertically oriented text of interest on the object of interest.

Abstract: This application related to automatic processes for identifying and extracting information from images of documents of varying layouts. For example, a computing device may receive an image of a document, where the image includes a plurality of color channels. The computing device applies a character recognition process to the image to generate optical character recognition data. Further, the computing device determines an area of the image that includes one or more characters based on the optical character recognition data. The computing device adjusts a value of each of a plurality of pixels corresponding to the area of the image determined for each character based on a value of each corresponding character to generate a modified image. The computing device then applies a trained machine learning process to the modified image to generate output data. The output data characterizes characters, such as words and number values, within the original image.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to train a machine learning algorithm to detect text of interest. In some embodiments, there is provided a system to detect vertically oriented text of interest including a first data set comprising a plurality of captured digital images each depicting an object of interest and a second data set comprising a plurality of augmented digital images each depicting a captured digital image augmented with a synthetic text image; a first control circuit configured to cause the machine learning algorithm to output a machine learning model trained to automatically detect occurrences of vertically oriented text of interest based on the first data set and the second data set; at least one camera; and a second control circuit configured to execute the machine learning model to automatically detect vertically oriented text of interest on the object of interest.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to determine text on an object. In some embodiments, there is provided a system to determine text of interest on an object of interest including at least one camera and a control circuit configured to execute a machine learning model trained to identify the text of interest, group into a cluster each node point that is located substantially in the same location in the text of interest, determine a score value of each particular character in the cluster, identify the particular character that has a determined score value corresponding to at least a threshold score value relative to all characters in the cluster, assign the particular character having the determined score value corresponding to at least the threshold score value as a recognized character in the cluster, and transmit to a display monitor overlay data.

Abstract: A method to couple a media content with a browser to enable the browser and web page to be aware of the interactions performed by the user on the pop-up window. The method of coupling enables the browser to render even a Media Source extension (MSE) video on the pop-up window ensuring browser's capability to track user interactions on the pop-up window.