Methods and devices for generating a training sample, training a model and recognizing a character are provided. The method for generating a training sample comprises: acquiring an image of characters, and determining respective characters contained in the image; and using a projection method to determine weights of the respective characters contained in the image, tagging the image with labels according to the weights of the respective characters contained in the image, and forming a training sample. The method for training a model comprises: using the training sample to train a character recognition model. The method for recognizing a character comprises: using the character recognition model to perform character recognition. The above methods and devices realize accurate recognition of characters, such as double-half characters, contained in an image of a wheel-type meter, and can provide a highly accurate biased recognition result.

Methods, systems, and computer storage media are provided for data extraction. A target document representation may be generated based on modified text of a target electronic document. A measure of similarity may be determined between the target document representation and a reference document representation, which may be based on modified text of a reference electronic document. Based on the measure of similarity, the reference document representation may be selected. An extraction model associated with the selected reference document representation can then be used to extract data from the target document.

Systems, devices, methods, and computer-readable media for evaluation and visualization of machine learning data drift. A method can include receiving a series of data indicating accuracy and confidence associated with classification of respective batches of input samples, and dynamically displaying, on the GUI, a concurrent plot of the accuracy and confidence as the series of data are received.

Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.

A semantic similarity fingerprint is generated for an image by inferring a plurality of SKUs each at an associated first weight based upon analysis of the image using the machine learning models. The associated first weights for each of the classifications based upon each machine learning model is the semantic similarity fingerprint. The semantic similarity fingerprint may be compared to previously generated semantic similarity fingerprints. If a match is found with a semantic similarity fingerprint that has previously been identified as a particular.

According to the present disclosure, a handwriting image and a background image are combined, thereby generating a combined image, a correct answer label image is generated based on the handwriting image, and the generated combined image and the generated correct answer label image are used as learning data for training a neural network.

The present invention provides a data network, a data ingestion system and a method of data ingestion in the data network for a supply chain management enterprise application. The data network includes one or more data objects of different data types received from different data sources structured on multiple distinct architecture, connected to each other for executing multiple functions in the enterprise application.

A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

A fingerprint authentication device includes: a sensor unit configured to output a sensing signal by sensing a fingerprint; an image processing unit configured to generate a fingerprint image based on the sensing signal; a storage unit configured to store a template including an enrolled image; and a learning unit configured to generate a first pseudo image and add the first pseudo image to the template.