The present disclosure relates to a tag-based font recognition system that utilizes a multi-learning framework to develop and improve tag-based font recognition using deep learning neural networks. In particular, the tag-based font recognition system jointly trains a font tag recognition neural network with an implicit font classification attention model to generate font tag probability vectors that are enhanced by implicit font classification information. Indeed, the font recognition system weights the hidden layers of the font tag recognition neural network with implicit font information to improve the accuracy and predictability of the font tag recognition neural network, which results in improved retrieval of fonts in response to a font tag query. Accordingly, using the enhanced tag probability vectors, the tag-based font recognition system can accurately identify and recommend one or more fonts in response to a font tag query.

In some implementations, a device may receive an image that depicts handwritten text. The device may determine that a section of the image includes the handwritten text. The device may analyze, using a first image processing technique, the section to identify subsections of the section that include individual words of the handwritten text. The device may reconfigure, using a second image processing technique, the subsections to create preprocessed word images associated with the individual words. The device may analyze, using a word recognition model, the preprocessed word images to generate digitized words that are associated with the preprocessed word images. The device may verify, based on a reference data structure, that the digitized words correspond to recognized words of the word recognition model. The device may generate, based on verifying the digitized words, digital text according to a sequence of the digitized words in the section.

The present embodiments relate to identifying a native language of text included in an image-based document. A cloud infrastructure node (e.g., one or more interconnected computing devices implementing a cloud infrastructure) can utilize one or more deep learning models to identify a language of an image-based document (e.g., a scanned document) that is formed of pixels. The cloud infrastructure node can detect text lines that are bounded by bounding boxes in the document, determine a primary script classification of the text in the document, and derive a primary language for the document. Various document management tasks can be performed responsive to determining the language, such as perform optical character recognition (OCR) or derive insights into the text.

Provided are a method for training a font generation model, a method for establishing a font library, and a device. The method for training a font generation model includes the following steps. A source-domain sample character is input into the font generation model to obtain a first target-domain generated character. The first target-domain generated character is input into a font recognition model to obtain the target adversarial loss of the font generation model. The model parameter of the font generation model is updated according to the target adversarial loss.

Disclosed are a method and a system for determining a response to a digital task in a computer-implemented crowd-sourced environment. The method comprises determining if a number of the plurality of responses to the digital task received meets a pre-determined minimum answer threshold; in response to the number of the plurality of responses to the digital task meeting the pre-determined minimum answer threshold, executing: for each of the plurality of responses generating, by the server, a confidence parameter representing a probability of an associated one of the plurality of responses being correct; ranking the plurality of responses based on the confidence parameter to determine a top response being associated with a highest confidence parameter; and in response to the highest confidence parameter being above a pre-determined minimum confidence threshold, assigning a value of the top response as a label for the digital task and terminating the digital task execution.

Utilizing a visual-feature-classification model to generate font maps that efficiently and accurately organize fonts based on visual similarities. For example, extracting 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, magnifying areas of font maps by mapping some fonts within a bounded area to positions within a higher-resolution font map. Additionally, navigating the font map to identify visually similar fonts (e.g., fonts within a threshold similarity).

An information processing apparatus includes a processor configured to acquire a result of character recognition of a character string formed on a medium and read by scanning that is subject to character recognition and replace a character or a symbol in a subject with a reference character string that is referred to by the character or the symbol.

A method for generating training data for hand-printed text recognition includes obtaining a structured document, obtaining a set of hand-printed character images and database metadata from a database, generating a modified document page image, and outputting a training file. The structured document includes a document page image that includes text characters and document metadata that associates each of the text characters to a document character label. The database metadata associates each of the set of hand-printed character images to a database character label. The modified document page image is generated by iteratively processing each of the text characters. The iterative processing includes determining whether an individual text character should be replaced, selecting a replacement hand-printed character image from the set of hand-printed character images, scaling the replacement hand-printed character image, and inserting the replacement hand-printed character image into the modified document page image.

A computer-implemented method for generating feedback based on a handwritten text, comprises the steps of initializing a writing instrument to be used in a writing operation comprising a handwritten text and capturing and processing the handwritten text to generate digital text data. The method further comprises the steps of identifying at least one handwritten text attribute associated with the digital text data, comparing the at least one handwritten text attribute with predefined textual feature attributes, and generating a textual feature based on the compared at least one handwritten text attribute and predefined textual feature attributes. In addition, the method comprises the steps of modifying the digital text data using the textual feature and generating feedback to a user based on the modified digital text data.

An information processing apparatus inspecting printed contents of a printed sheet, the information processing apparatus includes a recognition unit configured to recognize the printed contents printed on the sheet, an acquisition unit configured to acquire an attribute value from information recognized by the recognition unit, a specification unit configured to specify a time required for inspection of the printed contents of the sheet by using the attribute value acquired by the acquisition unit, and a notification unit configured to issue a notification to a user based on a result of comparison between the required time specified by the specification unit and a time limit for inspection of the printed contents.