Character recognition processing suitable to a handwritten character area and a printed character area among character areas in a scanned image of a document is performed. Next, character recognition results for the handwritten character area and character recognition results for the printed character area are integrated and a likelihood indicating a probability of being an extraction target is calculated for a candidate character string that is an extraction candidate among the integrated character recognition results and a character string that is the item value is determined. Then, at the time of the determination, different evaluation indications are used in a case where a character originating from the handwritten character area is included in characters constituting the candidate character string and in a case where such a character is not included.

A method and apparatus for complementing a depth image are provided. The method includes obtaining a color image and a corresponding depth image, obtaining a first depth image based on the color image, using a first deep neural network (DNN), obtaining a second depth image based on the depth image and an intermediate feature image generated by each intermediate layer of the first DNN, using a second DNN, and obtaining a final depth image by merging the first depth image and the second depth image.

Multiple field of view (FOV) systems are disclosed herein. An example system includes a bioptic barcode reader having a target imaging region. The bioptic barcode reader includes at least one imager having a first FOV and a second FOV and is configured to capture an image of a target object from each FOV. The example system includes one or more processors configured to receive the images and a trained object recognition model stored in memory communicatively coupled to the one or more processors. The memory includes instructions that, when executed, cause the one or more processors to analyze the images to identify at least a portion of a barcode and one or more features associated with the target object. The instructions further cause the one or more processors to determine a target object identification probability and to determine whether a predicted product identifies the target object.

This invention concern a system (1) for recognizing online handwriting comprising:—a handwriting instrument (2) including a body (3) extending longitudinally between a first end (4) and a second end (5), the first end (4) having a writing tip (6) which is able to write on a support, the handwriting instrument (2) further including a module (17) comprising at least one motion sensor (7) configured to acquire motion data on the handwriting of a user when the user is calculating unit (8) communicating with the at least one motion sensor (7) and configured to analyze the motion data by a machine learning model trained in a multitask way such that it is capable of performing at least two tasks at the same time, the machine learning model being configured to deliver as an output the sequence of characters which was written by the user with the handwriting instrument.

Tracking a current and/or previous position, velocity, acceleration, and/or heading of an object using sensor data may comprise determining whether to associate a current object detection generated from recently received (e.g., current) sensor data with a previous object detection generated from formerly received sensor data. In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. However, multiple types of sensor data may be used to detect objects and some objects may not be detected by different sensor types or may be detected differently, which may confound attempts to track an object. An ML model may be trained to receive outputs associated with different sensor types and/or a track associated with an object, and determine a data structure comprising a region of interest, object classification, and/or a pose associated with the object.

The disclosure relates to system and method for extracting software development requirements from natural language information. In one example, the method may include receiving structured text data related to a software development and derived from natural language information, extracting a plurality of features for each sentence in the structured text data, and determining a set of requirement classes and a set of confidence scores for the each sentence, based on the plurality of features, using a set of classification models. The method may further include deriving a final requirement class and a final confidence score for the each sentence based on the set of requirement classes and the set of confidence scores for the each sentence corresponding to the set of classification models, and providing the software development requirements based on the final requirement class and the final confidence score for the each sentence.

Some implementations of the disclosure are directed to: extracting metadata from textual data representations of a plurality of document images, and contextualizing the extracted metadata; storing the extracted metadata and the textual data representations in a full text index database; and transferring the extracted metadata and the textual data representations from the full text index database to a search engine platform, the search engine platform indexing and storing the transferred extracted metadata to allow for searching of the indexed, extracted metadata, the indexed, extracted metadata having been correlated to the textual data representations, where the search engine platform allows for the selection of extracted metadata stored in full text index database that is transferred to the search engine platform.

The present disclosure is directed to systems and methods for analyzing digital images to determine alphanumeric strings depicted in the digital images. An electronic device may generate a set of filtered images using a received digital image. The electronic device may also perform an optical character recognition (OCR) technique on the set of filtered images, and may filter out any of the set of filtered images according to a set of rules. The electronic device may further identify a set of common elements representative of the alphanumeric string depicted in the digital image, and determine a machine-encoded alphanumeric string based on the set of common elements.

The subject matter of this specification can be implemented in, among other things, a method that includes receiving a first image from a first camera depicting a first view of a physical item, where the physical item displays a plurality of characters. The method includes receiving a second image from a second camera depicting a second view of the physical item. The method includes performing optical character recognition on the first image to identify first characters and a first layout in the first image and on the second image to identify second characters and a second layout in the second image. The method includes combining the first characters with the second characters by comparing the first characters with the second characters and the first layout with the second layout. The method includes storing the combined first and second characters.

There is provided an image-based information extraction model, method, and apparatus, a device, and a storage medium, which relates to the field of artificial intelligence (AI) technologies, specifically to fields of deep learning, image processing, computer vision technologies, and is applicable to optical character recognition (OCR) and other scenarios. A specific implementation solution involves: acquiring a to-be-extracted first image and a category of to-be-extracted information; and inputting the first image and the category into a pre-trained information extraction model to perform information extraction on the first image to obtain text information corresponding to the category.