A method and an apparatus for presenting a candidate character string and a method and an apparatus for training a discriminative model are provided. The method for presenting a candidate character string may include: acquiring a character string of alphabetic words inputted by a user; determining a character transition weight of the character string, the character transition weight being used to characterize a transition probability of a character; generating a sort weight corresponding to the character string based on a pre-acquired basic weight matching the character string and the character transition weight; and selecting, according to an order indicated by the sort weight, at least two candidate character strings matching the character string from a pre-acquired candidate character string set for presentation.

An iris recognition apparatus includes a front cover in which a module mounting hole is formed, a cover plate configured to be coupled to a front surface of the front cover, and an iris recognition module configured to be fitted into the module mounting hole from the rear of the front cover. The iris recognition module includes a shield cover fitted into the module mounting hole, a red-eye phenomenon generator including at least one illumination part, a substrate on which the at least one illumination part is mounted and disposed on the rear of the shield cover, an imaging module including a camera module for iris recognition, a substrate on which the camera module is mounted and disposed on the rear of the red-eye phenomenon generator, and a light passage hole formed in the shield cover corresponding to the front of the at least one illumination part.

An iris recognition apparatus includes a front cover in which a module mounting hole is formed, a cover plate configured to be coupled to a front surface of the front cover, and an iris recognition module configured to be fitted into the module mounting hole from the rear of the front cover. The iris recognition module includes a shield cover fitted into the module mounting hole, a red-eye phenomenon generator including at least one illumination part, a substrate on which the at least one illumination part is mounted and disposed on the rear of the shield cover, an imaging module including a camera module for iris recognition, a substrate on which the camera module is mounted and disposed on the rear of the red-eye phenomenon generator, and a light passage hole formed in the shield cover corresponding to the front of the at least one illumination part.

The present invention aims to provide an identification assistance system, an identification assistance client, and an identification assistance method that enable the user to identify drugs accurately and easily. In the identification assistance system according to an aspect of the present invention, first text which is the result of voice recognition is corrected, and thus errors of the voice recognition can be corrected. In addition, the first text is corrected with reference to a drug search dictionary having learned expressions used for drug identification, and thus expressions unique to drug identification can be taken into consideration. The user can perform a search not only by using the code and/or the name of the drug but also by speaking aloud the external appearance information on the drug. Thus, even if the code and the name are unknown, the user can perform a search by using the external appearance information.

Methods, apparatus, and processor-readable storage media for character-based representation learning for table data extraction using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying, from unstructured documents comprising tabular data, items of text and corresponding document position information using artificial intelligence-based text extraction techniques; generating an intermediate output by implementing character embedding with respect to the unstructured documents using an artificial intelligence-based encoder; determining structure-related information for the unstructured documents using one or more artificial intelligence-based graph-related techniques by inferring columns from the tabular data; generating a character-based representation of the unstructured documents using an artificial intelligence-based decoder by converting the inferred columns into one or more line items; classifying portions of the character-based representation using artificial intelligence-based statistical modeling techniques; and performing one or more automated actions based on the classifying.

A computer-implemented method of executing a programmed spend management computer system. The computer system comprises a data pre-processor that is communicatively coupled to a plurality of the application instances and accesses historic transaction data from any of the instances and thereby has access to a large community of data across all tenants. The data pre-processor is programmed to normalize transaction descriptions and determine line spend values, unit price values, quantity values, and buyer country data for a plurality of commodities, and to store the data in item sets in digital storage. A statistical processor is coupled to the digital storage to access the item sets and executes statistical calculation on the item sets to generate pricing insight data. Pricing insights and/or prescriptions are generated automatically under stored program control and provided to a presentation processor for output to and/or rendering to an end-user device.

Methods, apparatus, and processor-readable storage media for character-based representation learning for table data extraction using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying, from unstructured documents comprising tabular data, items of text and corresponding document position information using artificial intelligence-based text extraction techniques; generating an intermediate output by implementing character embedding with respect to the unstructured documents using an artificial intelligence-based encoder; determining structure-related information for the unstructured documents using one or more artificial intelligence-based graph-related techniques by inferring columns from the tabular data; generating a character-based representation of the unstructured documents using an artificial intelligence-based decoder by converting the inferred columns into one or more line items; classifying portions of the character-based representation using artificial intelligence-based statistical modeling techniques; and performing one or more automated actions based on the classifying.

A method for text-image integration is provided. The method may include receiving a question related to pairable data comprising text data and image data. Embeddings are generated from the text tokens and image encodings. Embeddings are generated from the text tokens and image encodings. The embeddings include text embeddings and image embeddings. A spectral conversion of the text embeddings and the image embeddings is performed to generate spectral data. The spectral data is processed to extract text-image features. The text-image features are processed to generate inferred answers to the question.

Systems and methods for estimating freight costs are disclosed herein. The system receives, from a user interacting with the system, a first document and a second document, where an actual value is associated with the second document. Further, the system determines a set of variables for each of the first document and the second document based on a statistical analysis of historical data. Furthermore, the system estimates, using a trained artificial neural network model, a cost associated with each of the first document and the second document based on the determined set of variables. The cost associated with the first document includes an estimated freight cost for the first document, and the cost associated with the second document includes an estimated true value for the second document.

A computer-implemented method for adaptively discretizing a position of a textual object in a document includes receiving, by a computer system, an image of the document and determining, by the computer system, an absolute position of the textual object in the image of the document. The method further includes normalizing, by the computer system, the absolute position to determine a relative position of the textual object. The method also includes calculating, by the computer system, a bin size such that at least one axis of the image is divided into a plurality of separate bins, wherein a distance between each bin along the at least one axis and its adjacent bin equals the bin size. The method includes discretizing, by the computer system, the relative position based on the bin size to determine a discretized position of the textual object; and providing, by the computer system, the discretized position and a textual content of the textual object as an input to a machine learning model.