Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately enhancing optical character recognition with a machine learning approach for determining words from reverse text, vertical text, and atypically-sized text. For example, the disclosed systems segment a digital image into text regions and non-text regions utilizing an object detection machine learning model. Within the text regions, the disclosed systems can determine reverse text glyphs, vertical text glyphs, and/or atypically-sized text glyphs utilizing an edge based adaptive binarization model. Additionally, the disclosed systems can utilize respective modification techniques to manipulate reverse text glyphs, vertical text glyphs, and/or atypically-sized glyphs for analysis by an optical character recognition model.

Abstract: Techniques described herein implement heading identification and classification for a digital document in a digital medium environment. A document analysis system is leveraged to extract structural features from a digital document, identify heading candidates from among the structural features, validate the headings candidates, and classify validated headings into different headings types. The classified headings are then utilized to generate a sectioned version of the digital document (“sectioned document”) that is divided into different sections based on the headings. Further, a document directory is generated that includes the headings and that enables navigation to different sections of the sectioned document.

Abstract: Techniques described herein implement heading identification and classification for a digital document in a digital medium environment. A document analysis system is leveraged to extract structural features from a digital document, identify heading candidates from among the structural features, validate the headings candidates, and classify validated headings into different headings types. The classified headings are then utilized to generate a sectioned version of the digital document (“sectioned document”) that is divided into different sections based on the headings. Further, a document directory is generated that includes the headings and that enables navigation to different sections of the sectioned document.

Abstract: 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.

Abstract: A computer implemented method and apparatus for enabling text editing in a scanned document while maintaining fidelity of appearance of the text. The method comprises creating a synthesized font comprising a plurality of characters using characters present in a scanned document; replacing the plurality of characters in the scanned document with characters from the plurality of characters from the synthesized font; and enabling editing of the scanned document wherein enabling editing comprises adding at least some characters from the plurality of characters of the synthesized font to the document for at least some characters added during editing.

Abstract: The present disclosure is directed toward systems and methods for assisting users in correcting OCR errors. For example, systems and methods described herein involve identifying the position of a cursor within a machine-readable document. Systems and methods described herein also involve identifying corresponding position co-ordinates in a source image, as well as, capturing an image preview from the source image based on the corresponding position co-ordinates. Systems and methods described herein may also involve providing the preview of the source image within the machine-readable document.

Abstract: The present disclosure is directed toward systems and methods for assisting users in correcting OCR errors. For example, systems and methods described herein involve identifying the position of a cursor within a machine-readable document. Systems and methods described herein also involve identifying corresponding position co-ordinates in a source image, as well as, capturing an image preview from the source image based on the corresponding position co-ordinates. Systems and methods described herein may also involve providing the preview of the source image within the machine-readable document.

Abstract: A computer implemented method and apparatus for enabling text editing in a scanned document while maintaining fidelity of appearance of the text. The method comprises creating a synthesized font comprising a plurality of characters using characters present in a scanned document; replacing the plurality of characters in the scanned document with characters from the plurality of characters from the synthesized font; and enabling editing of the scanned document wherein enabling editing comprises adding at least some characters from the plurality of characters of the synthesized font to the document for at least some characters added during editing.