The information processing device acquires a probability image representing a probability of an existence of a character in each of pixels included in a target image including a plurality of characters based on the target image, estimates positions of respective character images included in the target image based on the acquired probability image, classifies the plurality of character images into a plurality of groups based on the estimated positions, acquires a plurality of recognition target images which is generated so as to correspond to the plurality of groups, and includes the plurality of character images respectively belonging to the corresponding groups, and recognizes the plurality of characters from each of the recognition target images.

The information processing device obtains a character string image which includes a plurality of characters, and which includes the characters arranged in an arrangement direction, obtains a probability image representing a probability of an existence of a character in each of pixels included in the character string image, obtains a plurality of character regions in which the characters are estimated to respectively exist in the character string image based on the probability image, obtains an additional character region which is located in the character string image, and which does not overlap the plurality of character regions based on a determination result on whether or not a pixel of a non-background color exists in a direction perpendicular to the arrangement direction at every position in the arrangement direction in the character string image, and recognizes the plurality of characters from the character regions and the additional character region.

A scalable system and method for completing a model task using a serverless architecture is disclosed. The system may include memory storing instructions and one or more processors. The method may include receiving a request to complete a model task; retrieving a first model and a first hyperparameter based on the request; provisioning computing resources to a first development instance configured to train the first model based on the first hyperparameter and the model task; training, by the first development instance, an instance of the first model to produce a trained model and terminating said training upon satisfaction of a training criterion; receiving the trained model and a first performance metric; receiving a second performance metric associated with a second model; and terminating the development instance based on a determination that the termination condition is satisfied based on at least one of the first and second performance metrics.

Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Systems and methods for determining neural network brittleness are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a modeling request comprising a preliminary model and a dataset. The operations may include determining a preliminary brittleness score of the preliminary model. The operations may include identifying a reference model and determining a reference brittleness score of the reference model. The operations may include comparing the preliminary brittleness score to the reference brittleness score and generating a preferred model based on the comparison. The operations may include providing the preferred model.

According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that when executed by the processor, may cause the processor to receive an image and identify contents in the received image. The processor may identify candidate regions on the image at which a predefined object is placeable. In some examples, the processor may assign scores to the identified candidate regions based on relative positions of the identified candidate regions to respective ones of the identified contents in the image. Based on the assigned scores, the processor may select a candidate region among the identified candidate regions at which the predefined object is to be placed. The processor may determine a size and a position of the predefined object based on the selected candidate region, and may output the determined size and the position of the predefined object on the image.

Systems and methods for clustering data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving data from a client device and generating preliminary clustered data based on the received data, using a plurality of embedding network layers. The operations may include generating a data map based on the preliminary clustered data using a meta-clustering model. The operations may include determining a number of clusters based on the data map using the meta-clustering model and generating final clustered data based on the number of clusters using the meta-clustering model. The operations may include and transmitting the final clustered data to the client device.

A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools.

An article reading device according to an embodiment includes a display device and an image capturing device that generates an image of an article. A processor extracts, from the image, first feature data for recognizing the article and second feature data for determining whether to recognize the article based on the first feature data. The processor determines whether to recognize the article. If it is determined to recognize the article, the processor recognizes the article based on the extracted first feature data, and controls the display device to display a recognition result. If it is determined to not recognize the article, extract a barcode from the image, the processor identifies the article based on the extracted barcode, and control the display device to display an identification result. The processor performs a transaction settlement with respect to the recognition result, if any, and the identification result, if any.