In implementations of systems for generating layouts of text objects, a computing device implements a layout system to receive input data describing a sequence of text and the layout system represents the sequence of the text as a number of text objects. A candidate layout for the text objects is identified from layout data describing a plurality of layouts, the candidate layout identified at least partially based on the number of the text objects. The layout system generates a binary tree having leaf nodes and at least one parent node representing the candidate layout. An arrangement of the text objects in the candidate layout is generated for display in a user interface based on the binary tree.

In implementations of systems for generating layouts of text objects, a computing device implements a layout system to receive input data describing a sequence of text and the layout system represents the sequence of the text as a number of text objects. A candidate layout for the text objects is identified from layout data describing a plurality of layouts, the candidate layout identified at least partially based on the number of the text objects. The layout system generates a binary tree having leaf nodes and at least one parent node representing the candidate layout. An arrangement of the text objects in the candidate layout is generated for display in a user interface based on the binary tree.

Examples herein disclose obtaining regions of digital content and determining a correlation measurement between the multiple regions of digital content adjacently located to each other. The examples disclose identifying a breakpoint in the digital content based on the determined correlation measurement.

A method for suggesting revisions to a document-under-analysis from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts, the method for suggesting revisions including selecting a statement-under-analysis (“SUA”), selecting a first original text of the plurality of original texts, determining a first edit-type classification of the first original text with respect to its associated final text, generating a first similarity score for the first original text based on the first edit-type classification, the first similarity score representing a degree of similarity between the SUA and the first original text, selecting a second original text of the plurality of original texts, determining a second edit-type classification of the second original text with respect to its associated final text, generating a second similarity score for the second original text based on the second edit-type classification, the second similarity score representing a degree of similarity between the SUA and the second original text, selecting a candidate original text from one of the first original text and the second original text, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.

A method for suggesting revisions to a document-under-analysis from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts, the method for suggesting revisions including selecting a statement-under-analysis (“SUA”), selecting a first original text of the plurality of original texts, determining a first edit-type classification of the first original text with respect to its associated final text, generating a first similarity score for the first original text based on the first edit-type classification, the first similarity score representing a degree of similarity between the SUA and the first original text, selecting a second original text of the plurality of original texts, determining a second edit-type classification of the second original text with respect to its associated final text, generating a second similarity score for the second original text based on the second edit-type classification, the second similarity score representing a degree of similarity between the SUA and the second original text, selecting a candidate original text from one of the first original text and the second original text, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.

A method and system for generating an automation engineering project in a technical installation is provided. The method includes receiving, by a processing unit, a request to generate an automation engineering project for a technical installation. The method further includes generating a first name graph based on the information about the hardware configuration associated with the automation engineering project. The method further includes generating, by the processing unit, a second name graph based on the analysis of the one or more modifications of the hardware configuration of the technical installation. The method further includes generating, by the processing unit, the automation engineering project from the plurality of engineering objects based on a comparison of the first name graph and the second name graph.

A method and system for generating an automation engineering project in a technical installation is provided. The method includes receiving, by a processing unit, a request to generate an automation engineering project for a technical installation. The method further includes generating a first name graph based on the information about the hardware configuration associated with the automation engineering project. The method further includes generating, by the processing unit, a second name graph based on the analysis of the one or more modifications of the hardware configuration of the technical installation. The method further includes generating, by the processing unit, the automation engineering project from the plurality of engineering objects based on a comparison of the first name graph and the second name graph.

The technology disclosed relates to systems and methods for device-dependent display of an article from a PDF file that has multiple articles and a table of contents to the articles. The system can use a library to render the article from the PDF file. The rendering can include bounding boxes positioned at on-page coordinates that can include one or more images and multiple text blocks of glyphs. The system can detect at least one table in the PDF file that includes pages numbers and multiple columns. The system includes logic to partition a contiguous sequence of text representing the table into text blocks of entries and columns. The system includes logic to merge multiple text blocks that align horizontally with a single page number into a single text block. Table of contents is displayed in a device-dependent format including the entries from the merged text blocks.

The technology disclosed relates to systems and methods for device-dependent display of an article from a PDF file that has multiple articles and a table of contents to the articles. The system can use a library to render the article from the PDF file. The rendering can include bounding boxes positioned at on-page coordinates that can include one or more images and multiple text blocks of glyphs. The system can detect at least one table in the PDF file that includes pages numbers and multiple columns. The system includes logic to partition a contiguous sequence of text representing the table into text blocks of entries and columns. The system includes logic to merge multiple text blocks that align horizontally with a single page number into a single text block. Table of contents is displayed in a device-dependent format including the entries from the merged text blocks.

Recommendation and approval of a header for a message includes generating a proposed header based on the name and/or brand of the entity and product and/or content of the message, classifying the proposed header using a machine learning model trained based on historical complaints on previously used headers related to the entity name and brand and product and/or content of the message and recommending the proposed header based on the classification. The training of the machine learning model may include learning a threshold wherein headers having a classification greater than the threshold are not recommended as having a high probability of being wrongly associated with the requesting entity and headers having a classification lower than the threshold are recommended as having a high probability of not being wrongly associated with the requesting entity.