Systems and methods are provided for laying out reflowable content for display, such as content of an electronic book, in a manner that may differ from spacing properties originally defined in a corresponding reflowable file. When an amount of spacing that the reflowable file indicates should be included in a given line exceeds a threshold that is set based at least in part on the available line display length, the layout may be adjusted to decrease the total spacing for the line without adjusting the display size of reflow objects within the line. The adjusted layout may include utilizing a scaled down amount of space relative to the spacing properties defined in the reflowable file, or applying a different alignment type than is indicated by the reflowable file.

A User Interface (UI) interface object detection system employs an initial dataset comprising a set of images, that may include synthesized images, to train a Machine Learning (ML) engine to generate an initial trained model. A data point generator is employed to generate an updated synthesized image set which is used to further train the ML engine. The data point generator may employ images generated by an application program as a reference by which to generate the updated synthesized image set. The images generated by the application program may be tagged in advance. Alternatively, or in addition, the images generated by the application program may be captured dynamically by a user using the application program.

Systems and methods are provided for laying out reflowable content for display, such as content of an electronic book, in a manner that may differ from spacing properties originally defined in a corresponding reflowable file. When an amount of spacing that the reflowable file indicates should be included in a given line exceeds a threshold that is set based at least in part on the available line display length, the layout may be adjusted to decrease the total spacing for the line without adjusting the display size of reflow objects within the line. The adjusted layout may include utilizing a scaled down amount of space relative to the spacing properties defined in the reflowable file, or applying a different alignment type than is indicated by the reflowable file.

Techniques described herein take character glyphs as input and generate a text-on-a-path text object that includes the character glyphs arranged in a determined order along a path. For instance, a method described herein includes accessing character glyphs in input data. The method further includes determining an order for the character glyphs based on relative positions and orientations of the character glyphs in the input data. The method further includes generating a path for the character glyphs, based on the order, and associating the path with the character glyphs. Further, the method includes generating a text object that includes the set of character glyphs arranged in the order along the path.

Method and apparatus to match bounding boxes around text to align forms. The approach is less computationally intensive, and less prone to error than text recognition. For purposes of achieving alignment, information per se is not as important as information location. Information within the bounding boxes is not as critical as is the location of the area which the bounding boxes occupy. Scanning artifacts, missing characters, or noise generally do not affect bounding boxes themselves so much as they do the contents of the bounding boxes. Thus, for purposes of form alignment, the bounding boxes themselves are sufficient. Using bounding boxes also avoids misalignment issues that can result from stray marks on a page, for example, from holes punched in a sheet, or from handwritten notations.

A method of extracting meta-data from semi structured documents, by using area and cone orientation as relevance between words/phrases is described. It also a computer implemented system to handle OCR errors with respect to the coordinates interpreted for each word and user corrections both in online and offline mode. The method is carried out by the steps as follows: converting scanned or digital document to a readable format with coordinates using OCR; scanning the coordinates obtained through OCR for each character; marking all potential labels and values with a bounding box; searching for relevant labels for the particular value by using default control parameters and adjusting trainable parameters; mapping a cone region for the labels and values using the upper and lower angles along x-axis and the scope box and formulating the score area to get the confidence percentage which is used as measure to extract all relevant label-value pairs.

An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Embodiments for finding elements in optical character recognition (OCR) documents are provided. An indication of a selected portion of document is received. Salient pixels in the selected portion of the document are determined. Properties of the salient pixels in the selected portion of the document are identified. The properties of the salient pixels in the selected portion of the document are compared to properties of pixels in each of a plurality of portions of an OCR-converted version of the document. A cognitive analysis is utilized to select at least some of the plurality of portions of the OCR-converted version of the document as suspected matches to the selected portion of the document.

An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.

Techniques described herein implement identifying artifacts in digital documents in a digital medium environment. A document analysis system is leveraged to extract page features from a digital document and to determine whether certain page features represent page artifacts such as headers and footers. Those page features determined to be page artifacts can be extracted from the digital document to generate a reflowed version of the digital document that preserves primary content. The primary content, for instance, is rearranged in the reflowed document to compensate for the extracted page artifacts.