An example method of document classification comprises: detecting a set of keypoints in an input image; generating a set of keypoint vectors, wherein each keypoint vector of the set of keypoint vectors is associated with a corresponding keypoint of the set of keypoints; extracting a feature map from the input image; producing a combination of the set of keypoint vectors with the feature map; transforming the combination into a set of keypoint mapping vectors according to a predefined mapping scheme; estimating, based on the set of keypoint mapping vectors, a plurality of importance factors associated with the set of keypoints; and classifying the input image based on the set of keypoints and the plurality of importance factors.

A computer implemented method of performing single pass optical character recognition (OCR) including at least one fully convolutional neural network (FCN) engine including at least one processor and at least one memory, the at least one memory including instructions that, when executed by the at least processor, cause the FCN engine to perform a plurality of steps. The steps include preprocessing an input image, extracting image features from the input image, determining at least one optical character recognition feature, building word boxes using the at least one optical character recognition feature, determining each character within each word box based on character predictions and transmitting for display each word box including its predicted corresponding characters.

Software User Assistance (UA) is afforded from captured User Interface (UI) screen images, with reference to persisted Machine Learning (ML) models. The captured screen images are processed—e.g., using rasterization, Optical Character Recognition (OCR), and/or establishment of a coordinate system—with individual UI elements being determined therefrom. Referencing the persisted ML models, the software application/application state for the captured image is identified. UA data relevant to that application/application state is generated from the model, and then provided to the user (e.g., in a text box overlying the UI screen). Through the capture and processing of UI screen images, embodiments afford a homogenous UA experience for installation, maintenance, and/or upgrade of heterogeneous members of a larger overall landscape, over software lifecycles. Embodiments may be deployed locally on a frontend computer, in order to avoid exporting UI images due to privacy and/or security concerns.

The present disclosure provides an information extraction method and apparatus, an electronic device and a readable storage medium, and relates to the field of natural language processing technologies. The information extraction method includes: acquiring a to-be-extracted text; acquiring a sample set, the sample set including a plurality of sample texts and labels of sample characters in the plurality of sample texts; determining a prediction label of each character in the to-be-extracted text according to a semantic feature vector of each character in the to-be-extracted text and a semantic feature vector of each sample character in the sample set; and extracting, according to the prediction label of each character, a character meeting a preset requirement from the to-be-extracted text as an extraction result of the to-be-extracted text. The present disclosure can simplify steps of information extraction, reduce costs of information extraction and improve flexibility and accuracy of information extraction.

The present disclosure provides an information extraction method and apparatus, an electronic device and a readable storage medium, and relates to the field of natural language processing technologies. The information extraction method includes: acquiring a to-be-extracted text; acquiring a sample set, the sample set including a plurality of sample texts and labels of sample characters in the plurality of sample texts; determining a prediction label of each character in the to-be-extracted text according to a semantic feature vector of each character in the to-be-extracted text and a semantic feature vector of each sample character in the sample set; and extracting, according to the prediction label of each character, a character meeting a preset requirement from the to-be-extracted text as an extraction result of the to-be-extracted text. The present disclosure can simplify steps of information extraction, reduce costs of information extraction and improve flexibility and accuracy of information extraction.

Embodiments of the present disclosure include a method that obtains a digital image. The method includes extracting a word block from the digital image. The method includes processing the word block by evaluating a value of the word block against a dictionary. The method includes outputting a prediction equal to a common word in the dictionary when a confidence factor is greater than a predetermined threshold. The method includes processing the word block and assigning a descriptor to the word block corresponding to a property of the word block. The method includes processing the word block using the descriptor to prioritize evaluation of the word block. The method includes concatenating a first output and a second output. The method includes predicting a value of the word block.

Systems and methods for optical character recognition using specialized confidence functions. An example method comprises: receiving a grapheme image; computing a feature vector representing the grapheme image in a space of image features; and computing a confidence vector associated with the grapheme image, wherein each element of the confidence vector reflects a distance, in the space of image features, between the feature vector and a center of a class of a set of classes.

Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

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.

A method of identifying characters in images extracts features of a detection image including characters. Enhancement processing is performed on the detection image according to the features to obtain an enhanced image. Closed edges of the characters are detected in the enhanced image. First rectangular outlines of the characters are determined according to the closed edges. The first rectangular outlines are corrected to obtain second rectangular outlines. The characters are cropped from the detection image according to the second rectangular outlines. The method identifies characters in images accurately and rapidly.