1092 results about "Typeface" patented technology

Systems and methods for generating custom art fonts with consistent style include receiving user input that identifies a base font style for a custom font and includes descriptive text that defies one or more text effects to use for the custom font. Depth maps are selected for characters to be included in the custom font. The depth maps are preprocessed to add noise to the depth maps. A generative model generates custom font images conditioned with the text prompt and the depth maps. The custom font images are then used to render text on a display screen of a computing device.

The invention relates to a stylized font generation method based on a diffusion model, and aims to solve the problems of font stroke structure disorder and style deviation in the prior art and realize accurate and stylized font generation. The method is divided into two stages: a first stage, performing pre-training of a font style classification task, including constructing a font style label, designing a style feature extraction network, and training by using a data set to obtain a pre-trained style encoder so as to extract font style features; and in the second stage, training of the generation model is started, style encoder parameters are frozen in the process, content features, stroke order structure features and style features serve as conditions, model parameters are optimized through mean square loss and contrast loss, and therefore a high-quality generation image is trained and generated. According to the method, the structural accuracy and style expressive force of font generation are remarkably improved, and efficient technical support and application value are provided for font library construction and design.

Techniques for creation and personalization of composite fonts are described. In one embodiment, a method includes receiving an input font sequence comprising font embeddings for a first font and sequence information for the first font, the font embeddings comprising numerical vectors, predicting a second font based on the font embeddings of the first font and the sequence information for the first font using a transformer-based machine learning model, selecting a character from the second font, and adding the character of the second font to a character of the first font to generate a set of characters for a composite font. Other embodiments are described and claimed.

The invention discloses a product package planning-oriented AI-driven full marketing content design method, and relates to the technical field of marketing content design, and the method comprises the following steps: obtaining a product package design draft, extracting color, composition, font and semantic features based on a deep convolutional neural network, and carrying out the fusion to form a brand tonality mother vector of brand style tonality; calling a multi-modal analysis module, extracting visual and language features consistent with brand tonality vector dimensions from multi-modal marketing materials published by a brand recently frame by frame or sentence by sentence, and generating a marketing material feature vector set; according to the method, the brand tonality mother vector is constructed as the style anchor point, and the multi-modal material analysis, the time-sensitive style evaluation mechanism and the self-adaptive style jump strategy are combined, so that the content style consistency is monitored in real time, the capability of dynamically regulating and controlling the style according to the content fatigue risk is realized, and the real-time monitoring of the content style consistency is realized. And the content generation system is ensured to intelligently sense the change of the propagation environment and make a strategic response.

The invention discloses a dynamic data processing and recognition method and device for a mixed font text, and belongs to the technical field of optical character recognition, and the method comprises the following steps: collecting a mixed font text image to be processed, and inputting the mixed font text image into an enhanced DBNet detection network to position a text region; integrating a lightweight MobileNetV3 font classification sub-network, sharing bottom layer features with a detection trunk, training a multi-task network through a joint loss function, and outputting a text region with a font type label; then selecting identification branches according to the labels, and identifying the printed form by using the traditional CRNN; according to the handwritten form, traditional convolution is replaced by linear variable convolution, kernel dynamic scaling is guided by a thermodynamic diagram, and multi-head attention mechanism recognition is embedded in a BiLSTM layer; and finally, results are fused, and detection and identification results with labels are output. According to the method, the mixed font text data processing and recognition precision is improved, the adaptability and robustness of handwritten text recognition are enhanced, and various text recognition requirements are met.

A data processing system implements constructing a first prompt including a font mask of a reference character (RC) and a style prompt, sending the first prompt to a text2image model to iteratively generate salient content and concentrate the salient content within the font mask of RC as a first image of RC; concatenating two of the first images as a second image; generating a combined font mask of the font mask of RC and a font mask of a target character (TC); constructing a second prompt including the combined font mask and the second image, sending the second prompt to the model to iteratively generate salient content and in-paint the salient content within a half of the combined font mask as a third image of RC and TC; cropping a styled TC image from the third image using the font mask of TC; providing the styled TC image to a client device.

The invention provides a semantic input-based artistic font generation method, which comprises the following steps of: acquiring text data required by artistic font generation, and performing word segmentation processing on a text by adopting a word segmentation method to obtain a keyword list; performing semantic extension on each keyword in the keyword list according to a pre-constructed HowNet knowledge base to obtain synonyms, hypernyms and hyponyms of the keywords; performing word frequency statistics and TF-IDF weight calculation on the expanded keyword list, and screening out a core semantic concept required by the keyword art font generation with the highest weight; calculating semantic similarity among the core semantic concepts, and constructing a semantic similarity matrix; taking the screened core semantic concepts as nodes and the semantic similarity as weights of edges, and constructing a semantic network atlas; k-means clustering analysis is carried out on the semantic concept vectors, concepts with high semantic similarity are clustered according to a clustering result, and a semantic vector representation model required by artistic font generation is constructed.

The invention belongs to the technical field of image processing, and particularly relates to a handwriting elimination method and system based on fusion layout structure and semantic knowledge. The method comprises the following steps: inputting a to-be-processed document image into a semantic segmentation model, carrying out pixel point segmentation on the to-be-processed document image one by one, and eliminating handwritten forms to obtain a first intermediate document image; utilizing a layout analysis model to determine layout structure information and text information in the first intermediate document image so as to obtain restoration information used for printed form restoration; performing error correction on the text blocks in the restoration information to obtain a second intermediate document image; and performing printed form restoration on the second intermediate document image to output a text image only containing a printed form. According to the method, font missing, font missing and wrong fonts can be effectively repaired, and the influence caused by shooting conditions and a model elimination effect is solved.

The invention discloses an unstructured file identification method based on primitive identification, and the method comprises the following steps: (1) preprocessing: cutting and zooming an electrical wiring drawing to a required size, and carrying out the gray processing; (2) primitive recognition: constructing a feature extraction network of an electrical primitive detection model by adopting a YOLO electrical primitive detection algorithm fused with an attention mechanism; (3) character recognition, which comprises the following steps: removing primitives, detecting an annotated text area, constructing a text candidate box, segmenting and adjusting an annotated text boundary, recognizing annotated text content, and filtering an electrical text recognition result; and (4) electrical element association relationship analysis, which comprises the following steps: frame element extraction, primitive group region division, template matching and electrical primitive-annotation text association. According to the method, the primitives and characters of the electrical wiring diagram can be obtained, the problem that different fonts and styles appear in marked characters can be solved, and semantic information can be understood.

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for automated propagation of glyph edits to corresponding adornments. In embodiments, an indication to attach an adornment to a glyph associated with a variable font is identified. Thereafter, an anchor point indicating a point of attachment of the adornment to the glyph is identified. Based on obtaining an indication to modify a variable font property associated with the glyph, an edit to apply to the adornment is determined. Generally, the edit maintains an alignment of the adornment with a modified anchor point reflecting the modified variable font property associated with the glyph. Thereafter, the edit is applied to the adornment.

To provide a suitable game machine.SOLUTION: In a game in which a counter value regarding a difference between a used game value and an imparted game value restricts progress of a game if an operation value is reached when no control is performed to an advantageous state, and restricts progress of a game after completion of the advantageous state when the operation value is reached during control to the advantageous state, prior notification is performed when the counter value reaches a prior notification value, and operation warning notification is performed if the counter value reaches the operation value when control is performed to the advantageous state, the font of a character used for prior notification is the same as the font of a character used for the operation warning notification, and the font of a character used for the prior notification and the operation warning notification differs from the font of a character used for error notification.SELECTED DRAWING: Figure 15-226

The invention relates to the field of character recognition systems, and discloses a Chinese ancient book character recognition method based on an image recognition technology, which comprises the following steps: reading an ancient book image according to an open source code computer vision library to obtain an original image; performing gray processing on the original image to obtain an image after gray equalization; according to a median filtering algorithm, carrying out de-noising processing on the image after gray scale equalization to obtain a pre-processed image; according to the method, multi-level feature extraction is carried out on the image through the real-time multi-scale detection model and the convolutional neural network, and character features of different scales and angles in the image can be captured; and therefore, the character recognition accuracy is improved, especially for the common font, typesetting, inclination or blurring conditions in ancient book images.

The invention discloses an insurance customer service picture text information structured extraction method based on a multi-modal large model. The method comprises the following steps: S1, training a text corpus and a font sample by adopting a multi-modal training strategy to obtain a preliminary OCR large model; s2, completing the construction of a general field picture data set; s3, completing the construction of a business domain picture data set; s4, integrating the general field picture data set and the business field picture data set, performing further training based on a pre-trained OCR large model, and generating an insurance customer service multi-modal large model for insurance customer service scene optimization; and S5, deploying the insurance customer service multi-mode large model after fine adjustment to a GPU server. According to the method, through an end-to-end multi-modal large model architecture, a dynamic data training strategy and a real-time optimization mechanism, the efficiency, the accuracy and the adaptation capability of structured extraction of picture text information in an insurance customer service scene are remarkably improved.

A prompt for a generative artificial intelligence (GenAI) model is received which includes unicode. Unicode fonts in the prompt are identified and then translated into a plaintext representation. Further, unicode characters in the prompt are identified which each have an associated unicode tag. It is determined, based on the associated unicode tags, whether at least a portion of the unicode characters are valid. When at least a portion of the unicode characters are determined to be valid, the unicode characters in the prompt are converted into a plaintext representation. The prompt with the translated fonts and the converted unicode fonts are passed into the GenAI model. When at least a portion of the unicode characters are not determined to be valid, the unicode characters are removed from the prompt. This prompt with the translated unicode fonts, after the unicode characters are removed, is input into the GenAI model.

The invention discloses an intelligent completion code optimization method based on texture pre-rendering and multi-level cache, which comprises the following steps of: pre-establishing a grammar rule set and a GPU cache characteristic database, storing a grammar style sheet when a code editor is started, generating a font image set and loading current GPU cache parameters; after a candidate code segment list pops up through intelligent completion, dividing candidate code segments into data blocks, analyzing syntax types, determining character styles, and generating candidate code segment textures through instantiation rendering and a fragment shader; according to the current GPU cache parameter, the selected priority of the candidate code segment and the compressed volume, the texture is distributed to a first-level buffer, a second-level buffer or a global video memory according to a rule, and metadata is recorded and added into a global texture cache table; and after the user selects the candidate code segment, reading texture data from the corresponding GPU buffer, calculating a display coordinate by combining a cursor row and column index, an editor basic style parameter and a DPI scaling, completing texture rendering and drawing, and realizing optimized display of the intelligent completion code.

The disclosure provides a method and apparatus for generating a word, an electronic device, and a storage medium. The method for generating a word includes: obtaining images corresponding to a word to be processed and a reference word; and inputting the image to be processed into a target font style fusion model to obtain a target word of the word to be processed in a target font style. The target font style is determined based on the target font style fusion model fusing a reference font style of the reference word and a font style to be processed of the word to be processed.

The invention discloses a chapter-level Chinese ancient book image restoration method and related equipment, and belongs to the field of ancient book restoration. The method comprises the following steps: acquiring ancient book images, and screening out intact ancient book images and damaged ancient book images; for an intact ancient book image, simulating damage in a random mask mode, and constructing a training data pair; for the damaged ancient book image, marking the position of the incomplete character, and determining the specific content of the incomplete character; constructing a repair model based on the diffusion model, and training the repair model; and using the trained model to gradually realize the restoration of the chapter-level ancient book image in the form of an overlapped sliding window in an autoregression mode. According to the method, on the basis of the damaged positions and the damaged text content of the ancient books marked by human experts, the diffusion model technology is adopted, an autoregressive restoration method is combined, automatic restoration of the chapter-level damaged ancient book image is achieved, and the overall consistency of the font style and the background is effectively kept in the restoration process.

An annotation method and an electronic device are provided. The method includes: An electronic device displays content of a first file in a first interface. The electronic device detects a preset operation in the first interface, and displays the second interface including a first region and a second region. The first region is used to display the content of the first file, the second region is used to display annotation content. A font format and / or a paragraph format of at least a part of words in the first region are / is different from a font format and / or a paragraph format of words in the first file in the first interface.

The invention discloses a PDF (Portable Document Format) document structured extraction system based on a multi-modal language model, belongs to the technical field of document processing and optical character recognition, and aims at solving the technical problem of how to improve the existing OCR (Optical Character Recognition) technology to improve the analysis capability of a complex document structure and improve the recognition precision of handwritten forms and other non-standard fonts. According to the technical scheme, the system adopts a layered decoupling architecture and comprises an input layer, a preprocessing layer, a reasoning layer, an output layer and a monitoring and fault-tolerant module; wherein the output layer is used for multi-source data access and path management to realize a local file system or S3 cloud storage; the preprocessing layer is used for invalid document filtering and visual feature extraction; the reasoning layer is used for multi-modal model interaction and content processing; the output layer is used for outputting a content aggregation result; and the monitoring and fault-tolerant module is used for realizing real-time state monitoring, resource consumption analysis and exception handling.

The invention relates to the technical field of UI design, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a font rendering optimization method, device, equipment and medium. The font rendering optimization method comprises the following steps: identifying position coordinates of a target character in a document object structure and determining a context environment type of the target character and a view area range where the target character is located; generating a font file request based on the target character and the context environment type, obtaining an adaptive font data packet, inputting a rendering model matched with the context environment type, establishing a binding relationship with the view area range, monitoring a display state change event of the view area range, and triggering a font resource updating operation according to event parameters. And obtaining incremental font data and configuring the incremental font data to font rendering parameters of the rendering model. According to the method, the character context environment and the view area state change are combined, the font data corresponding to the target character are requested and loaded on the client according to needs, and refined distribution and real-time updating of font resources are achieved.

The invention provides a calligraphy work automatic identification and evaluation method and system, and the method comprises the steps: collecting calligraphy pattern images of different calligraphers, fonts and styles, and forming a multi-dimensional labeling original data set; carrying out standardization processing on the acquired calligraphy images; extracting stroke-level manual statistical features by using edge detection and contour analysis based on the standardized sample, extracting high-dimensional deep features by using a convolutional neural network, and fusing to form a unified multi-dimensional style feature vector; and performing unsupervised clustering on the fusion feature vectors of all the samples, and establishing a high-dimensional style prototype library. Obtaining a multi-dimensional similarity vector of a newly input calligraphy image; performing semantic unification processing on the new sample; and performing real-time scoring output on the new sample by using the exclusive evaluation model. The method can efficiently and accurately identify and evaluate the calligraphy work, is high in adaptability, and can be widely applied to the fields of calligraphy teaching, calligraphy work evaluation, digital calligraphy management and the like.

In one aspect, a computerized method of automatically generating suggestions for a graphic design using a one click virtual button using an artificial-intelligence (AI) assistant for generating one or more contextually relevant graphics designs includes the step of receiving a digital design in a digital design tool. The digital design tool comprises the one click virtual button using the AI assistant for generating the one or more contextually relevant graphic designs. The method includes the step of detecting an event trigger for the one click virtual button. The method includes the step of with the AI assistant. The AI assistants operations includes determining and suggesting a set of alternate background images for the digital design, determining and suggesting a set of alternate font types, determining and suggesting a set of color replacements, determining and suggesting a set of alternate layout suggestions of multiple elements in the scene, determining and suggesting a set of icon replacements, determining and suggesting a set of graphs and chart replacements, determining and suggesting a set of resizes of the digital design, and determining and suggesting a set of alternate collage configuration. The method includes the step of enabling a user to selection at least of the as a user selection. The method includes the step of integrating the user selection into an updated version of the digital design. The method includes the step of displaying the updated version of the digital design in the digital design tool.

Apparatuses, methods, and systems for a computer-implemented method for electronic message generation are disclosed. One method includes receiving, by a server, an input description from a user, scraping, by the server, characteristics of other electronic messages of the user including at least colors and fonts, generating, by the server, N message sections based on the input description and the scraped characteristics, applying, by the server, colors, fonts, and designs to the generated N message sections, filtering, by the server, the generated N message sections to eliminate message sections that include pre-identified undesired characteristics, displaying, by the server, the filtered electronic message sections to the user, and receiving, by the server, feedback from the user regarding the displayed filtered electronic message sections.

The invention relates to the technical field, in particular to an AI classroom teaching quality evaluation method and system based on image recognition, and the method comprises the steps: obtaining a test paper RGB image and structured metadata, and generating multi-modal input data through graying, denoising and affine transformation; dynamically adjusting the weight through a double-branch feature extraction network (CNN + ViT), and extracting the global structure of the printed form and the local detail features of the handwritten form; generating a mixed font segmentation mask by using space attention, channel attention and dynamic routing regulation; optimizing handwritten answer recognition in combination with an educational knowledge graph; and fusing the answer expression and the attention data to generate a teaching quality evaluation report. The system comprises a data acquisition unit, a preprocessing unit, a feature extraction unit, a segmentation unit, an identification verification unit and an evaluation unit. According to the scheme, the accuracy of character segmentation and recognition in a mixed font scene is improved, and data support is provided for precise teaching in an education scene.

The invention discloses an unmanned vehicle formation automatic control method and system based on a large language model. The method comprises the steps that an obtained task instruction is analyzed, task semantics are obtained, and the task semantics comprise the number of unmanned vehicles, task targets and font styles; extracting a task formation in the task target, and determining a task level according to the task formation; determining a target external tool based on the task level; acquiring a corresponding control strategy based on the target external tool; and automatically controlling the unmanned vehicle formation according to the control strategy. According to the method, key information such as the number of unmanned vehicles and task targets can be accurately extracted, and a control strategy can be dynamically adjusted according to the complexity and importance of tasks, so that formation control can flexibly adapt to various different types of task requirements; an intelligent strategy generation mechanism ensures the collaboration and efficiency of the unmanned vehicle formation during task execution, and the operation efficiency, reliability and formation accuracy are effectively improved.

The invention provides a teaching document anti-copy protection method and system based on OCR interference resistance, and belongs to the technical field of information security and digital copyright protection. According to the method, anti-OCR font processing is applied to document characters, invisible disturbance symbols are inserted between characters or paragraphs, watermark information is embedded in document typesetting or font strokes, and finally a standard PDF or DOCX file is compiled. Watermark information is embedded through stroke thickness change, paragraph row spacing indentation perturbation and other modes. The system comprises a font disturbance module, a character disturbance module, a watermark embedding module, a compiling output module and a verification module. According to the method, the human eyes can normally read the document, but messy codes or disorder can occur when the OCR tool extracts the document, and the document cannot be directly reused; meanwhile, watermark information cannot be stripped, so that leakage tracking is facilitated; and the output file keeps a standard format and is compatible with the existing office teaching process. The method is suitable for anti-copying protection of teaching documents, scientific research data, test papers, contract files and the like.

The invention belongs to the technical field of natural language processing, and discloses a natural language processing platform and method based on deep learning, and the platform comprises a data collection module which is used for collecting font data, text data, image data and environment data; the data processing module is used for preprocessing the collected font data, text data, image data and environment data and obtaining a comprehensive feature data set; the character recognition module is used for predicting character recognition results through a character recognition model and judging whether the character recognition results are consistent or not; the semantic recognition module is used for generating corresponding character paraphrases through a semantic recognition model; collecting character recognition results and corresponding character paraphrases through the software terminal; the voice output module is used for converting the character recognition result and the paraphrases of the corresponding characters into natural voices and outputting the natural voices by adopting a TTS technology; through the implementation of the technology, students can be helped to better learn and understand.

The invention discloses a multi-agent multi-modal table question and answer method and system based on a cognitive collaboration mechanism. The method and system are particularly suitable for extracting accurate information from a complex, unstructured or multi-modal data source and answering questions of a user. The system is composed of four core modules, namely a task ontology analysis agent, a multi-modal clue perception agent, a logic verification agent and an arbitration fusion agent, and the task ontology analysis agent, the multi-modal clue perception agent, the logic verification agent and the arbitration fusion agent are integrated by receiving user questions and multi-modal table input. The intelligent agents cooperate to complete the processes of keyword extraction, intention recognition, vision-text cross-modal analysis, clue generation, logic verification, result fusion and the like, and an efficient cognitive closed loop is formed. The system not only supports operation on standard table data, but also can process visual clues such as complex header structures, cell merging modes, font styles and the like, so that the ability of understanding fuzzy or incomplete input is improved. Successful tests in a plurality of practical application scenes prove that the method has strong fault-tolerant capability and accuracy.

The invention provides a method for automatically identifying and extracting total element information of a scanned borehole histogram. The method comprises the following steps of: (1) initializing parameter configuration; (2) character sample manufacturing and model training: generating corresponding character samples in batches according to font information set in initialization parameters, and training and optimizing an OCR character recognition model; (3) making a special symbol sample; (4) extracting and processing total element information of the borehole histogram; and (5) formulating a structured storage standard of a drilling histogram total element information extraction result, and storing the structured storage standard in an XML (Extensible Markup Language) file format. The method is reasonable in conception, one-time batch extraction of the total element information of the borehole histogram is achieved, the accuracy of information such as characters and special symbols can be effectively improved through the adopted deep learning algorithm, the position relation between the image elements is established, it can be guaranteed that the complete information of the borehole histogram can be comprehensively recorded in the XML marking text, and the accuracy of the borehole histogram is improved. And the drawing information is not lost.

The invention discloses a character string fuzzy matching method and system, and relates to the field of character recognition, and the method comprises the steps: obtaining a plurality of characters of different font sizes and fonts, and constructing a font similarity matrix; in the replacement operation in the distance editing method, character similarity weighting is carried out on the cost of the replacement operation so as to construct a weighted distance editing method; a weighted editing distance is used for the two character strings to be matched, the obtained weighted editing distance is normalized, and the similarity of the two character strings to be matched is obtained; when the similarity is 1, the matching is successful; when the similarity is greater than a set similarity threshold value and less than 1, representing a suspicious matching result; when the similarity is smaller than a set similarity threshold value, the matching fails. The method can solve the problem that character errors exist in similar results recognized by the OCR technology.