Various methods, apparatuses/systems, and media for data processing are disclosed. A processor receives a digital document; applies an optical character recognition (OCR) algorithm on said received digital document by utilizing an OCR tool; identifies defective data extracted by the OCR tool resulted from relatively inferior image quality of the received digital document; implements an auto rectification algorithm on the identified defective data; automatically generates, in response to implementing the auto rectification algorithm, corresponding auto-rectified data for each identified defective data; records the defective data and corresponding auto-rectified data at a field level; receives user input data on said recorded auto-rectified data; determines whether the auto-rectified data is correct or not; and populates, based on determining that the auto-rectified data is correct, a machine learning model with said received user input data to be utilized for subsequently received digital document.

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.

Predicting trends may include obtaining trend data from one or more sources, extracting a plurality of trends from the trend data, and producing permutations combining terms or concepts appearing in the plurality of trends to create trend candidates. A first term from a first trend or concept in the plurality of trends may be combined with a second term or concept from a second trend in the plurality of trends.

Predicting trends may include obtaining trend data from two or more sources, extracting meaning from the trend data including meaning from a plurality of trends, and grouping trends from the plurality of trends such that trends that have equivalent meaning but not identical expression are grouped together as an aggregated trend.

An apparatus includes a memory and processor. The memory stores OCR and NLP algorithms. The processor receives an image of a physical document page and executes the OCR algorithm to convert the image into text. The processor identifies errors in the text, which are associated with noise in the image. The processor generates a feature vector that includes features obtained by executing the NLP algorithm on the text, and features associated with the identified errors in the text. The processor uses the feature vector to assign the image to a document category. Documents assigned to the document category share one or more characteristics, and the feature vector is associated with a probability greater than a threshold that the physical document associated with the image includes those characteristics. The processor then stores the image in a database as a page of an electronic document belonging to the assigned document category.

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.

Aspects of the disclosure generally relate to computing enabled systems and/or devices and may be generally directed to machine learning for computing enabled systems and/or devices. In some aspects, the system captures one or more digital pictures, receives one or more instruction sets, and learns correlations between the captured pictures and the received instruction sets.

A segmentation neural network is extended to provide classification at the segment level. An input image of a document is received and processed, utilizing a segmentation neural network, to detect pixels having a signature feature type. A signature heatmap of the input image can be generated based on the pixels in the input image having the signature feature type. The segmentation neural network is extended from here to further process the signature heatmap by morphing it to include noise surrounding an object of interest. This creates a signature region that can have no defined shape or size. The morphed heatmap acts as a mask so that each signature region or object in the input image can be detected as a segment. Based on this segment-level detection, the input image is classified. The classification result can be provided as feedback to a machine learning framework to refine training.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

A method and system are disclosed for training a model that implements a machine-learning algorithm. The technique utilizes latent descriptor vectors to change a multiple-valued output problem into a single-valued output problem and includes the steps of receiving a set of training data, processing, by a model, the set of training data to generate a set of output vectors, and adjusting a set of model parameters and component values for at least one latent descriptor vector in the plurality of latent descriptor vectors based on the set of output vectors. The set of training data includes a plurality of input vectors and a plurality of desired output vectors, and each input vector in the plurality of input vectors is associated with a particular latent descriptor vector in a plurality of latent descriptor vectors. Each latent descriptor vector comprises a plurality of scalar values that are initialized prior to training the model.