A web detection system processes webpage information and performs automated feature extraction of webpages including machine processable information. In an embodiment, the web detection system determines a subset of webpages having a target characteristic by processing markup language. For a webpage of the subset, the web detection system determines that a first image overlaps at least a portion of a second image in the webpage. The web detection system generates an image of the webpage such that the portion of the second image is obscured by the first image. The web detection system determines a graphical feature of the webpage by processing the image, e.g., using optical character recognition. Responsive to determining that the graphical feature corresponds to graphical features of images of a different set of webpages associated with a target entity, the web detection system determines that the webpage is also associated with the target entity.

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract a texture from embedded text within a digital image utilizing kerning-adjusted glyphs. For example, the disclosed systems utilize text recognition and text segmentation to identify and segment glyphs from embedded text depicted in a digital image. Subsequently, in some implementations, the disclosed systems determine optimistic kerning values between consecutive glyphs and utilize the kerning values to reduce gaps between the consecutive glyphs. Furthermore, in one or more implementations, the disclosed systems generate a synthesized texture utilizing the kerning-value-adjusted glyphs by utilizing image inpainting on the textures corresponding to the kerning-value-adjusted glyphs. Moreover, in certain instances, the disclosed systems apply a target texture to a target digital text based on the generated synthesized texture.

According to one embodiment, a learning apparatus includes a processor. The processor acquires a document to which a tag is added. The processor converts the document into an image to generate a document image, and converts the tag into an image according to composition of the document image to generate a tag image. The processor trains a network model using the document image as input data and the tag image as ground truth data to generate a trained model.

A text recognition method and apparatus that relate to the field of information processing technologies are provided. This effectively resolves a low recognition rate of curved text. The text recognition method includes: obtaining a to-be-detected image; determining a target text detection area in the to-be-detected image, where the target text detection area includes target text in the to-be-detected image, and the target text detection area is a polygonal area including m (m is a positive integer greater than 2) vertex pairs; correcting the polygonal area to m−1 rectangular areas to obtain a corrected target text detection area; and performing text recognition on the corrected target text detection area, and outputting the target text.

A method includes: obtaining a first image including a target item; selecting a plurality of reference images corresponding to the first image from a database; performing word segmentation on item text information corresponding to the plurality of reference images to obtain a plurality of words; and extracting a key word meeting a reference condition from the plurality of words, and determining the extracted key word as an item name of the target item.

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract a texture from embedded text within a digital image utilizing kerning-adjusted glyphs. For example, the disclosed systems utilize text recognition and text segmentation to identify and segment glyphs from embedded text depicted in a digital image. Subsequently, in some implementations, the disclosed systems determine optimistic kerning values between consecutive glyphs and utilize the kerning values to reduce gaps between the consecutive glyphs. Furthermore, in one or more implementations, the disclosed systems generate a synthesized texture utilizing the kerning-value-adjusted glyphs by utilizing image inpainting on the textures corresponding to the kerning-value-adjusted glyphs. Moreover, in certain instances, the disclosed systems apply a target texture to a target digital text based on the generated synthesized texture.

Described herein is a computer implemented method. The method includes accessing, by a computer system including a processing unit, portable document format (PDF) data defining a plurality of glyphs, sorting the plurality of glyphs into one or more glyph sets, and calculating an expanded glyph bounding box for each glyph. Each glyph set is processed to determine one or more text areas, each text area being associated with one or more glyphs from the glyph set which have collectively overlapping expanded bounding boxes.

Described herein is a computer implemented method. The method includes receiving data for an area of text of a document, the area of text containing a plurality of glyphs. A processing unit groups the glyphs into a plurality of lines based on glyph position information and for each line determines one or more paragraph attributes, wherein a difference in the one or more paragraph attributes between different lines indicates a likelihood that the different lines are in different paragraphs. Responsive to determining the one or more paragraph attributes, the plurality of lines are grouped into one or more paragraphs and an editable document is generated that contains text in paragraphs that corresponds to the one or more paragraphs.

Disclosed is an electronic device including: a memory, and a processor operatively connected to the memory. The memory stores instructions which, when executed, cause the processor to: obtain handwriting data including at least one letter; align the at least one letter with a reference line to generate target handwriting data; change at least one of a position or an angle of the at least one letter to generate distorted handwriting data; obtain correction information for correcting the distorted handwriting data to correspond to the target handwriting data; and store the correction information in the memory.

A system for determining compression rates for images, the system comprising a processing resource configured to: obtain a given image at least partially comprising a given text; compress the given image at a given compression ratio, giving rise to a compressed image; perform Optical Character Recognition (OCR) on the compressed image, giving rise to OCR text; compare the OCR text to the given text, giving rise to comparison results; upon the comparison results meeting a rule, increase the given compression rate; and upon the compression results not meeting a rule, return to a previous compression rate, if any.