According to implementations of the subject matter described herein, there is provided a solution for text recognition in an image. In this solution, a target text line area, which is expected to include a text to be recognized, is determined from an image. Probability distribution information of a character model element(s) present in the target text line area is determined using a single character model. The single character model is trained based on training text line areas and respective ground-truth texts in the training text line areas. Texts in the training text line areas are arranged in different orientations, and/or the ground-truth texts comprise texts are related to various languages (e.g., texts related to a Latin and an Eastern languages). The text in the target text line area can be determined based on the determined probability distribution information. The single character model enables more efficient and convenient text recognition.

A method for table extraction from journal literature based on text state characteristics is disclosed. The method includes: constructing a table model according to characteristics of tables in journal literature, where the table model includes two parts: a table caption and table content, building a text line set, table detection, table data positioning, table reconstruction, building a cell data set, restoring data of merged cells, checking the cell data set, and outputting table data. The method is particularly designed based on characteristics of tables such as three-line tables widespread in PDF journal literature, which can realize accurate and correct extraction of specific tables in the PDF journal literature, and especially can ensure a logic relationship of a three-line table. The whole process neither requires manual intervention or interaction nor requires table selection, so that the whole extraction process is automatic.

The present invention discloses a medical prediction method and system based on a semantic graph network, which recognizes an entity in an electronic medical record based on domain knowledge, and uses a two-way gated loop unit to learn a sequence features of a text. Secondly, in order to extract a semantic relation in the electronic medical record in a fine-granularity manner, the present invention defines two types of subgraphs, graph representation based on defined knowledge and graph representation based on undefined knowledge, and uses a Graph Convolution Network (GCN) and a Graph Attention Network (GAT) to extract a semantic relation representation, where the graph representation based on undefined knowledge allows the learning of a relation between an entity or an word and the graph representation based on undefined knowledge, and it also allows to learn a relation between word or entity and itself, in order to translate entity or word representation into a uniform graph embedding representation. For an attribute-value pair, the present invention uses a bi-directional gate recurrent unit (Bi-GRU) to extract an entity corresponding to a numerical feature or a categorical feature after extracting the numerical feature or the categorical feature in the electronic medical record to construct attribute-value graph representation. Finally, the semantic relation and an attribute-value are fused to train a prediction model of a disease level.

A first character string obtainment unit according to one or more embodiments may obtain a first character string in response to an input character string that has been input. A similar character extraction unit extracts similar characters having similar shapes as characters in the first character string. A second character string generation unit generates one or more second character strings in which some or all of the characters in the first character string are replaced with similar characters extracted by the similar character extraction unit. Then, a conversion candidate output unit outputs the first character string and the second character strings as conversion candidates for the input character string.

A first character string obtainment unit according to one or more embodiments may obtain a first character string in response to an input character string that has been input. A similar character extraction unit extracts similar characters having similar shapes as characters in the first character string. A second character string generation unit generates one or more second character strings in which some or all of the characters in the first character string are replaced with similar characters extracted by the similar character extraction unit. Then, a conversion candidate output unit outputs the first character string and the second character strings as conversion candidates for the input character string.

A method for generating an Artificial Intelligence (AI) bot comprises the steps of receiving (210) information of a human (3102), indicative of the physical characteristics including an appearance and vocals of the human (3102) and behavioural characteristics of the human (3102), analysing (220) the information for identifying and mimicking the vocals of the human (3102), analysing (230) the information for identifying and imitating the appearance of the human (3102), generating (240) the AI bot having the appearance of the human (3102) in a mixed reality space, processing and merging (250) the identified physical characteristics and the behavioural characteristics into the AI bot, displaying (260) the AI bot having physical characteristics and the behavioural characteristics of the human (3102) using the HMD (102), enabling (270) an interaction of the AI bot with users in the mixed reality space, thereby enabling the omnipresence of the human (3102).

A method for generating an Artificial Intelligence (AI) bot comprises the steps of receiving (210) information of a human (3102), indicative of the physical characteristics including an appearance and vocals of the human (3102) and behavioural characteristics of the human (3102), analysing (220) the information for identifying and mimicking the vocals of the human (3102), analysing (230) the information for identifying and imitating the appearance of the human (3102), generating (240) the AI bot having the appearance of the human (3102) in a mixed reality space, processing and merging (250) the identified physical characteristics and the behavioural characteristics into the AI bot, displaying (260) the AI bot having physical characteristics and the behavioural characteristics of the human (3102) using the HMD (102), enabling (270) an interaction of the AI bot with users in the mixed reality space, thereby enabling the omnipresence of the human (3102).

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 communication system includes circuitry. The circuitry receives an input of language information. The circuitry performs recognition on the input language information. The circuitry displays one or more images corresponding to the input language information on a display, based on a result of the recognition.