The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a visual-feature-classification model to generate font maps that efficiently and accurately organize fonts based on visual similarities. For example, the disclosed systems can extract features from fonts of varying styles and utilize a self-organizing map (or other visual-feature-classification model) to map extracted font features to positions within font maps. Further, the disclosed systems can also magnify areas of font maps by mapping some fonts within a bounded area to positions within a higher-resolution font map. Additionally, the disclosed systems can navigate the font map to identify visually similar fonts (e.g., fonts within a threshold similarity).

A textual properties model is used to infer values for certain font properties of interest given certain text-related data, such as rendered text images. The model may be used for numerous purposes, such as aiding with document layout, identifying font families that are similar to a given font families, and generating new font families with specific desired properties. In some embodiments, the model is trained from a combination of synthetic data that is labeled with values for the font properties of interest, and partially-labeled data from existing “real-world” documents.

An OCR system which acquires character data from a form (50) through OCR processing is characterized by: managing an OCR information table (34e) in which an issuer name of an issuer on the form (50) is associated with a font name of a font used in the OCR processing; and, when the OCR processing is performed on an issuer-recorded content reading target area in the form (50), performing the OCR processing (S156) in the font indicated by the font name associated in the OCR information table with the issuer name of the issuer of the form (50).

A method and a system for converting a font of a Chinese character in an image, a computer device and a medium are disclosed. A specific implementation of the method includes: acquiring a stroke of a to-be-converted Chinese character in the image and spatial distribution information of the stroke; and generating a Chinese character in a target font that corresponds to the to-be-converted Chinese character in the image according to the stroke of the to-be-converted Chinese character, the spatial distribution information of the stroke and standard stroke information of the target font, to replace the to-be-converted Chinese character.

A computer-implemented method for assessing if characters in a sample image are formed from a predefined font. The method comprises forming a first embedded space representation for the predefined font, extracting sample characters from the sample image, forming a second embedded space presentation of the sample characters, and comparing the first and second embedded space representation to assess if the sample characters are of the predefined font.

The disclosed technology is generally directed to optical character recognition for forms. In one example of the technology, optical character recognition is performed on a plurality of forms. The forms of the plurality of forms include at least one type of form. Anchors are determined for the forms, including corresponding anchors for each type of form of the plurality of forms. Feature rules are determined, including corresponding feature rules for each type of form of the plurality of forms. Features and labels are determined for each form of the plurality of forms. A training model is generated based on a ground truth that includes a plurality of key-value pairs corresponding to the plurality of forms, and further based on the determined features and labels for the plurality of forms.

There are provided a font creation apparatus, a font creation method, and a font creation program capable of generating, from small-number character images having a desired-to-be-imitated style, a complete font set for any language having the same style as the character images. A feature amount extraction unit (40) receives a character image (32) of a first font having a desired-to-be-imitated style and extracts a first feature amount of the first font of the character image (32). An estimation unit (42) estimates a transformation parameter between the extracted first feature amount and a second feature amount of a reference second font (34). A feature amount generation unit (44) generates a fourth feature amount of a second font set to be created by transforming a third feature amount of a complete reference font set (36) based on the estimated transformation parameter. A font generation unit (46) generates a complete second font set by converting the generated fourth feature amount of the second font set into an image.

Embodiments of the present invention are directed towards generating a captured font from an image of a target font. Character glyphs of the target font can be detected from the image. A character glyph can be selected from the detected character glyphs. A character mask can be generated for the selected character glyph. The character mask can be used to identify a similar font. A character from the similar font corresponding to the selected character glyph can be transformed to match the character mask. This transformed corresponding character can be presented and used to generate a captured font. In addition, a texture from the image can be applied to the captured font based on the transformed corresponding character.

Font recognition and similarity determination techniques and systems are described. In a first example, localization techniques are described to train a model using machine learning (e.g., a convolutional neural network) using training images. The model is then used to localize text in a subsequently received image, and may do so automatically and without user intervention, e.g., without specifying any of the edges of a bounding box. In a second example, a deep neural network is directly learned as an embedding function of a model that is usable to determine font similarity. In a third example, techniques are described that leverage attributes described in metadata associated with fonts as part of font recognition and similarity determinations.

Methods and systems for location-based digital font recommendations determine locations of the images and assign mappings between the identified digital fonts in the images and the locations of the images. Additionally, one or more embodiments detect a location related to content being viewed by a user. In response, one or more embodiments determine a location associated with the content and identify one or more digital fonts associated with the location from a font-location database. Based on the identified digital font(s), one or more embodiments provide a location-based recommendation of digital fonts for use in connection with the content.