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.

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.

Systems and methods of image searching include receiving content, receiving a request to select an image from content, selecting a plurality of items in the image, retrieving information about the selected item, and providing display data based on the retrieved information.

A laundry data analysis apparatus based on artificial intelligence according to an embodiment of the present invention includes: a communication unit configured to receive an image including laundry data related to characteristics of laundry from an image acquisition device corresponding to a group including at least one member; and a processor configured to recognize the laundry data from the received image, acquire additional data related to the characteristics of the laundry on the basis of the recognized laundry data, store laundry information including the laundry data and the additional data into a database, and acquire member characteristic information of each of the at least one member from a plurality of laundry information corresponding to the group stored in the database.

Aspects of the disclosure relate to training and using a phrase recognition model to identify phrases in images. As an example, a selected phrase list may include a plurality of phrases is received. Each phrase of the plurality of phrases includes text. An initial plurality of images may be received. A training image set may be selected from the initial plurality of images by identifying the phrase-containing images that include one or more phrases from the selected phrase list. Each given phrase-containing image of the training image set may be labeled with information identifying the one or more phrases from the selected phrase list included in the given phrase-containing images. The model may be trained based on the training image set such that the model is configured to, in response to receiving an input image, output data indicating whether a phrase of the plurality of phrases is included in the input image.

Techniques for sanitizing personally identifiable information (PII) from audio and visual data are provided. For instance, in a scenario where the data comprises an audio signal with speech uttered by a person P, these techniques can include removing/obfuscating/transforming speech-related PII in the audio signal such as pitch and acoustic cues associated with P's vocal tract shape and/or vocal actuators (e.g., lips, nasal air bypass, teeth, tongue, etc.) while allowing the content of the speech to remain recognizable. Further, in a scenario where the data comprises a still image or video in which a person P appears, these techniques can include removing/obfuscating/transforming visual PII in the image or video such as P's biological features and indicators of P's location/belongings/data while allowing the general nature of the image or video to remain discernable. Through this PII sanitization process, the privacy of individuals portrayed in the audio or visual data can be preserved.

Techniques are provided for determining a location of a mobile device based on visual positioning solution (VPS). An example method for determining a position estimate of a mobile device includes obtaining sensor information, detecting one or more identifiable features in the sensor information, determining a range to at least one of the one or more identifiable features, obtaining coarse map information, determining a location of the at least one of the one or more identifiable features based on the coarse map information, and determining the position estimate for the mobile device based at least in part on the range to the at least one of the one or more identifiable features.

An identity document can be authenticated using format data of a barcode on the document, such as a barcode on a driver's license. Scan data is obtained by decoding a plurality of barcodes. Format features of the plurality of barcodes are extracted. Scan data is classified into two or more clusters. Each cluster is characterized by a set of format features extracted from the scan data. A barcode on an ID to be verified is scanned. Format features from the barcode of the ID to be verified is compared to at least one of the two or more clusters to authenticate the ID.

The system for recognizing one or more objects of a design creative within an environment, analyzing the one or more objects using a deep neural networking model and generating a modified design creative by (i) determining a location of a design creative within the media content, (iii) determining an object from the design creative, (iv) determining an attribute of the object, (v) implementing a compliance rule to the attribute of the object to determine a distinctness and an effectiveness of a brand product, (vi) generating an attention sequence and heatmap for the media content, (vii) automatically generating a first recommendation based on the compliance rule, the attention heatmap, and the attention sequence, and (viii) automatically generating a modified design creative for the environment based on the attention heatmap, the attention sequence and the generated first recommendation using the deep neural networking model.

An image content determination device includes at least one processor, in which the processor is configured to execute first recognition processing of recognizing a character and a face of a first person from a first image including the character and the face of the first person, execute first acquisition processing of acquiring first person-related information related to the first person included in the first image based on the recognized character and face of the first person, execute second recognition processing of recognizing a face of a second person from a second image including the face of the second person, and execute second acquisition processing of acquiring second person-related information related to the second person included in the second image, in which the second person-related information is acquired using the first person-related information corresponding to the first image including the face of the first person similar to the face of the second person.