A method and a platform of generating a document, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a text recognition scenario and other scenarios. The method includes: performing a category recognition on a document picture to obtain a target category result; determining a target structured model matched with the target category result; and performing, by using the target structured model, a structure recognition on the document picture to obtain a structure recognition result, so as to generate an electronic document based on the structure recognition result, wherein the structure recognition result includes a field attribute recognition result and a field position recognition result.

A method and a platform of generating a document, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a text recognition scenario and other scenarios. The method includes: performing a category recognition on a document picture to obtain a target category result; determining a target structured model matched with the target category result; and performing, by using the target structured model, a structure recognition on the document picture to obtain a structure recognition result, so as to generate an electronic document based on the structure recognition result, wherein the structure recognition result includes a field attribute recognition result and a field position recognition result.

Method, computer readable medium, and apparatus of recognizing character zone in a digital document. In an embodiment, the method includes classifying a segment of the digital document as including text, calculating at least one parameter value associated with the classified segment of the digital document, determining, based on the calculated at least one parameter value, a zonal parameter value, classifying the segment of the digital document as a handwritten text zone or as a printed text zone based on the determined zonal parameter value and a threshold value, the threshold value being based on a selection of an intersection of a handwritten text distribution profile and a printed text distribution profile, each of the handwritten text distribution profile and the printed text distribution profile being associated with a zonal parameter corresponding to the determined zonal parameter value, and generating, based on the classifying, a modified version of the digital document.

Method, computer readable medium, and apparatus of recognizing character zone in a digital document. In an embodiment, the method includes classifying a segment of the digital document as including text, calculating at least one parameter value associated with the classified segment of the digital document, determining, based on the calculated at least one parameter value, a zonal parameter value, classifying the segment of the digital document as a handwritten text zone or as a printed text zone based on the determined zonal parameter value and a threshold value, the threshold value being based on a selection of an intersection of a handwritten text distribution profile and a printed text distribution profile, each of the handwritten text distribution profile and the printed text distribution profile being associated with a zonal parameter corresponding to the determined zonal parameter value, and generating, based on the classifying, a modified version of the digital document.

A method comprises identifying a representation of first statistical information in a document, identifying descriptive text that describes the first statistical information, determining whether the descriptive text accurately describes the first statistical information, and upon determination that the descriptive text does not accurately describe the first statistical information, generating alternative descriptive text that accurately describes the first statistical information.

In an example, a first set of text may be received from a client device. A set of content items may be selected from among content items based upon the first set of text and a plurality of sets of content item text associated with the content items. A set of terms may be determined based upon the first set of text and the set of content items. A similarity profile associated with the set of terms may be generated. The similarity profile is indicative of similarity scores associated with similarities between terms of the set of terms. Relevance scores associated with the set of terms may be determined based upon the similarity profile. One or more search terms may be selected from among the set of terms based upon the relevance scores. A search may be performed based upon the one or more search terms.

Improved techniques for combining human tasks with robotic tasks and/or external tasks in an organized manner to define an automation workflow process for use by a software automation system. A workflow process platform can assist a developer in creating an automation workflow process and/or managing performance of an automation workflow process. The automation workflow process can carry out a process, such as a business process, by interrelating human tasks performed by users with robotic tasks performed by computing machines or external tasks performed by applications (e.g., local or cloud-based). The workflow process platform can be network-based and utilize various users and computing machines that are affiliated with different groups (e.g., teams, departments) of an organization. Advantageously, the improved techniques can enable automation of business processes using various persons, robotic agents and/or applications in an organized and controlled manner.

Systems and methods for secure and efficient bill capture, analysis, and execution are provided. A method may include capturing, via a camera embedded in a smart card, an image of a bill. The bill may include a plurality of text fields. The method may include processing the text fields via a microprocessor embedded in the smart card. The method may include determining, based at least in part on the processing of the text fields, a balance amount and a payment recipient associated with the bill. The method may also include executing a payment for the balance amount from an account associated with a user of the smart card to an account associated with the payment recipient. The executing may be performed via a wireless communication element embedded in the smart card which may be configured to provide wireless communication between the smart card and a payment gateway.

Systems and methods for secure and efficient bill capture, analysis, and execution are provided. A method may include capturing, via a camera embedded in a smart card, an image of a bill. The bill may include a plurality of text fields. The method may include processing the text fields via a microprocessor embedded in the smart card. The method may include determining, based at least in part on the processing of the text fields, a balance amount and a payment recipient associated with the bill. The method may also include executing a payment for the balance amount from an account associated with a user of the smart card to an account associated with the payment recipient. The executing may be performed via a wireless communication element embedded in the smart card which may be configured to provide wireless communication between the smart card and a payment gateway.

A system and method for classifying digital images is presented. The method includes extracting a plurality of descriptive data items of a transaction evidence from a digital image indicating a plurality of purchased items; searching in data source for informative data based on the extracted plurality of descriptive data items, wherein the informative data includes a price; determining a correlated amount for each of at least one of the plurality of descriptive data items, wherein the correlated amount determined for one of the descriptive data items defines a paid price for the descriptive data item; determining, based on at least one expense type classification rule, a primary expense type of the transaction evidence, wherein the at least one expense type classification rule is applied to the plurality of descriptive data items and each of the correlated amount; and classifying the digital image based on the primary expense type.