131 results about "Text annotation" patented technology

Some examples of the disclosure are directed to object manipulators and associated processes for manipulating an object representation in a three-dimensional environment. The object representation may correspond to a scan of a real-world object in a real-world environment. The object manipulators may include an object cleanup manipulator and a text annotation manipulator. The object cleanup manipulator may be selectable to display one or more control affordances providing functionality for selectively removing portions of the object representation in the three-dimensional environment and / or selectively adjusting one or more parameters of the object representation in the three-dimensional environment. The text annotation manipulator may be selectable to display one or more control affordances providing functionality for selectively generating one or more text labels in the three-dimensional environment. The one or more text labels may be associated with the object representation in the three-dimensional environment.

A large language model (LLM) message generation system and method tor generating a response to a user message. The method includes: obtaining a text document specified by a user; chunking the text document into a plurality of chunks; generating text gloss data for each of the plurality of chunks based on the chunk and a predetermined prompt; storing the text gloss data for each chunk into a vector data store along with an identifier for the chunk and / or tine chunk itself; receiving a user message; querying the vector data store with a vector data store query, wherein the vector data store query is generated based on the user message; obtaining chunk(s) based on querying the vector data store with the vector data store query; generating a language model input based on the one or more identified chunks; and generating a response message based on inputting the language model input.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention discloses a method for constructing a nursing field text annotation corpus based on deep learning, and relates to the field of artificial intelligence technology and medical information processing. Comprising a data collection and preprocessing step, a BERT-based nursing field entity recognition model construction and training step, an automatic labeling and post-processing step, a corpus construction and management step and an application and intelligent support step. According to the method, the problems of efficiency and accuracy of text labeling in the nursing field are solved, and the continuously developing and changing text data processing requirements in the nursing field can be better met. The entity recognition model architecture specially aiming at the text characteristics in the nursing field is constructed based on deep learning, and challenges can be effectively handled when complex and diversified nursing text data is processed. And meanwhile, by utilizing the automatic labeling capability of deep learning, the labeling efficiency is improved, the manpower and time cost is reduced, and a new direction, a new mode and new experience are provided for nursing informatization development.

The invention discloses an ASR audio corpus generation method and device based on a multi-modal large model, and relates to the field of audio corpora. According to the method, a semantic vector and a condition vector are spliced into a joint vector, and first voice is generated; target noise is selected from a preset noise library according to the scene label, the target noise is superposed on the first voice to generate voice with noise, and adversarial noise is injected to generate second voice; performing noise labeling, text labeling, emotion labeling and speaker labeling on the second voice, and performing alignment to generate a multi-modal labeling file; and setting a word error rate threshold value and a semantic similarity threshold value according to the scene label, the noise type and the speaker information of the multi-modal labeling file, and screening a target corpus from the multi-modal labeling file according to the word error rate threshold value and the semantic similarity threshold value. By implementing the technical scheme provided by the invention, the audio corpus which is high in quality, meets specific requirements and is effectively screened can be generated.

The invention discloses a training method and device of a dialect recognition model and a dialect recognition method. The model training method comprises the following steps: training a first initial model comprising a feature extraction module, a voice encoder and a natural language large model by using a first dialect sample set with a text label to obtain a first dialect recognition model; training a second initial model containing a feature extraction module, a voice encoder and a voice decoder in the first dialect recognition model by using a text result of a first dialect sample set predicted by the first dialect recognition model to obtain a second dialect recognition model; and finally, training a second dialect recognition model by using the second dialect sample set without text annotation and a text result of the second dialect sample set predicted by the first dialect recognition model to obtain a target dialect recognition model. The technical problems that a large number of samples are needed in traditional dialect recognition model training, the data quality is difficult to guarantee, and the labeling cost is high are solved.

The invention provides a power grid regulation and control text data labeling method and system based on a large language model. According to the method, a complete process from data preprocessing to semantic analysis, annotation generation and question and answer generation is designed for unstructured power grid text data. By introducing the semantic understanding ability of a large language model, the system can efficiently extract key content in a text and generate accurate annotations and intelligent questions and answers. Experiments show that the method is remarkably superior to a traditional method in text labeling accuracy and efficiency, and is particularly suitable for processing requirements of large-scale text data such as power grid regulation and control instructions and operation logs.

The embodiment of the invention relates to an autoregressive language model for video generation. Embodiments are provided for autoregressively generating a video using a video generation model. An aspect includes a method including performing a progressive multi-stage training process including a first stage and a second stage, where: the first stage includes training a video generation model to perform text-to-image generation; and the second stage comprises further training the video generation model using a training dataset comprising tagged video-text pairs, wherein further training the video generation model comprises: for each tagged video-text pair: generating at least one text tag using a text markup device and a text annotation of the tagged video-text pair; generating a plurality of video tags using a video tokenizer and a video of the tagged video-text pair; generating a frame tag from regression using the at least one text tag; and training a video generation model using loss values calculated from the frame tags and the video tags.

The invention relates to an electrical main wiring diagram character recognition and classification method fusing CAD and image data, and the method specifically comprises the steps: converting a collected DWG format electrical main wiring diagram into a high-resolution JPG format drawing, and carrying out the preprocessing of the drawing through a Canny edge detection algorithm; performing text detection by using a target detection algorithm, distinguishing common texts and text labeling boxes in the drawing, and extracting coordinates and category information of all the text labeling boxes; collecting text content in an original DWG format electrical main wiring diagram, insertion point coordinates of the text content and end point coordinate data of a straight line entity, and carrying out standardization processing on all coordinate data and coordinate data of a target detection algorithm; pre-classifying the text annotation boxes of the texts according to the text annotation boxes and the endpoint data of the straight line entities in the corresponding coordinates; and obtaining an equipment text classification result and a complete device text according to a textbox classification result in combination with the endpoint coordinates and the text positions of the corresponding straight line entities.

The invention relates to a PID (Proportion Integration Differentiation) drawing element intelligent identification and topology reconstruction method based on visual detection, which comprises the following steps of: cooperatively extracting multi-modal information, detecting components based on improved YOLOv11, identifying all text labels in a graph by adopting PaddleOCR, and carrying out pipeline identification algorithm and merging filtering method based on probability Hough transform of pixel points. Identifying and defining the T-shaped connection point as a special topological node to obtain positioning information of components and characters in a PID drawing, associating the components with the characters by using a regular expression and an Euclidean distance, and converting image elements into a topological graph model with a semantic relationship; according to the method, the end-to-end automation process of detection-association-reconstruction is achieved, finally, display is conducted in a graphical interface mode, a data basis is provided for subsequent application such as drawing analysis, system simulation and equipment management, end-to-end automatic conversion is achieved, and efficiency is greatly improved.

The invention discloses a speech recognition method and device, a storage medium and equipment. The method comprises the following steps: firstly, obtaining a to-be-recognized target speech and extracting a target acoustic feature of the to-be-recognized target speech; inputting the target acoustic feature into a pre-constructed speech recognition model, and recognizing to obtain a speech hot word recognition result of the target speech; wherein the speech recognition model comprises an audio encoder, a hot word encoder and a decoder; the decoder includes a hotword hybrid expert adapter and an attention mechanism network. The end-to-end speech recognition large model is trained to generate the speech recognition model based on the adapter by using the sample speech data and the corresponding text labeling result and the sample hot word text, so that the model training efficiency is improved, the training cost is reduced, and the recognition accuracy of the model is also effectively improved, and therefore, the recognition efficiency of the speech recognition model is improved. When the voice recognition model is used for performing voice hot word recognition on the target voice, the efficiency and the accuracy of a recognition result can be effectively improved.

The invention discloses a text-based long-sequence dance action automatic generation method, which comprises the following steps of: data preparation: extracting human body actions in dance and carrying out text labeling, and making a text-dance task data set; model construction: constructing a long-sequence dance motion generation model; model training: training a long-sequence dance motion generation model by using the text-dance task data set; and dance generation: inputting the dance description text into the trained long-sequence dance motion generation model to obtain a generated motion sequence, and performing visual rendering to obtain a long-sequence dance motion video. According to the method, dance actions related to semantics can be automatically constructed from an unstructured natural language provided by a user, and high-quality action sequence modeling and generation are realized.

The invention relates to the technical field of data processing, and discloses an automobile fault code labeling method and device, computer equipment and a storage medium, and the automobile fault code labeling method comprises the steps: carrying out the recognition processing of fault maintenance data, generating structured data, carrying out the standardization processing of the structured data, and generating text verification data; fault code fields and state information are extracted based on the text verification data, preliminary annotation data are constructed, recognition enhancement processing is carried out according to the preliminary annotation data, and fusion annotation data are generated; carrying out maintenance part matching processing on the fusion annotation data to generate image-text annotation structure data; and performing format processing on the image-text annotation structure data to generate target automobile fault code annotation data. According to the method, the labeling capability of the complex maintenance data can be effectively improved, the manual operation burden is reduced, and the efficiency, accuracy and consistency of data labeling are greatly improved.

The invention discloses a webpage embedded text labeling method, which comprises the following steps that: the front end of a webpage labeling plug-in uses Javascript and Chrome browser native API (Application Program Interface) to respond to a labeling instruction of a labeling user on an original text through a document object model element and a browser object model element; a webpage labeling plug-in background identifies and labels original text corpora obtained by a front end by using a pre-preinstalled labeling pre-training model in a pre-labeling algorithm engine library, performs data storage by using a distributed storage engine Elasticsearch, and combines and stores a model pre-labeling result and a manual labeling result. According to the method, the input cost of text collection and text cleaning work in the aspects of manpower and time at the present stage is reduced, the text labeling process is simplified, the use threshold of a traditional labeling platform or tool is reduced, and a user can realize data screening and labeling while browsing a webpage.

The invention provides a lead mark arrangement method and device, electronic equipment and a storage medium, relates to the technical field of computers, in particular to the fields of engineering drawing, computer aided design and the like, and can be used for application scenes such as drawing lead mark arrangement and the like. According to the specific implementation scheme, the method comprises the steps of obtaining a plurality of to-be-arranged lead marks and a plurality of mark objects; aiming at each to-be-arranged lead mark, determining a lead-out point coordinate of a lead based on a corresponding mark object; in response to a text arrangement mode selected by a user, determining text coordinates according to the extraction point coordinates; and generating a lead mark arrangement result according to the lead-out point coordinate and the text coordinate corresponding to each to-be-arranged lead mark. According to the scheme, the lead text annotation arrangement can be quickly carried out, various arrangement effects can be obtained by setting a simple arrangement mode, and the operation efficiency of arrangement and annotation when a user makes a picture is greatly improved.

A method and apparatus comprising computer code configured to cause a processor or processors to receive a text comprising a plurality of sentences, by a machine learning model, extract a nonverbal message from one of the sentences and add an annotation to the text, the annotation indicating the nonverbal message, and output a version of the text including the annotation.

Disclosed is a method (100) and a system (200) for digitalizing and analysing an engineering diagram (310, 400A) for an industry environment. The method comprises converting raw data from the engineering diagram, including at least one of graphical symbols, textual annotations, and connections, into a structured digital data through an extraction process. The method further comprises mapping the structured digital data to a predefined standards-based ontology (314) to generate a knowledge graph (318, 400B), compliant with predefined semantic standards, corresponding to the engineering diagram, wherein the predefined standards-based ontology comprises entities and their corresponding attributes, and interrelations therebetween related to the engineering diagram. The method further comprises conducting graph-based analytics on the knowledge graph (318, 400B) to extract one or more parameters pertaining to the engineering diagram, for the industry environment.

The invention discloses a rapid text guidance sound effect generation method and system based on segmented rectification, and belongs to the field of sound effect generation. The method comprises the following steps: acquiring sound effect data with description text annotations, pre-training a diffusion model as a teacher diffusion model, training a student diffusion model by adopting distillation learning, taking a differential equation trajectory of the student diffusion model as a segmented linear trajectory, and calculating by adopting a linear interpolation mode to obtain noise-added Mel spectrogram features corresponding to sampling time steps; the student diffusion model is guided by text features of a description text, and noise is predicted for noise-added Mel spectrogram features corresponding to sampling time steps; in the sound effect generation stage, a user provides a description text and initializes noise, and the student diffusion model operates an inverse diffusion denoising process segment by segment and generates a final sound effect. According to the method, the segmented rectification technology is introduced into the sound effect generation diffusion model, so that rapid text-guided sound effect generation is realized, and the sound effect quality generated during low step number generation of the diffusion model is greatly improved.

The invention relates to the technical field of wind power generation, and discloses a wind power blade intelligent diagnosis method and system based on large model visual semantic fusion, and the method comprises the steps: obtaining a historical image of a target wind turbine blade, and carrying out the first text marking operation of the historical image; performing first coding and second coding on the historical image of the target wind turbine blade; carrying out feature fusion on the first coding result and the second coding result, taking a feature fusion result as input of a first classification model, and training the first classification model; and acquiring a real-time image of the target wind turbine blade, and performing fault diagnosis on the wind turbine blade in combination with the first classification model. According to the invention, image and text information are combined, and the accuracy and efficiency of fault diagnosis are improved. By introducing a multi-modal fusion model and fully utilizing semantic information described by a blade surface image and a fault text, finer fault detection is realized. By combining multi-modal feature mining of images and texts, efficient identification of common faults such as blade cracks, corrosion and falling is realized.

The present invention discloses a video text retrieval method based on differential multi-scale and multi-granularity feature fusion, which mainly solves the problem of low video text matching accuracy caused by the failure to fully utilize video temporal features and fine-grained information text annotation in the prior art. The implementation solution is as follows: obtaining a video frame sequence and a text annotation sequence; constructing a feature extraction network and extracting global and local features of the text annotation; differentiating the video frame features according to the time series and combining them with the frame features through a sequence feature extraction network to obtain local and global features of the video; calculating the global similarity and local similarity between the video and the text annotation, and calculating a loss function; training the network using the loss function; calculating the similarity between the video and the text annotation using the trained network and sorting to obtain the retrieval result. The present invention can reduce the semantic gap between different modalities, mine the temporal information in video modality data, improve the cross-modal retrieval accuracy, and can be used for video topic detection and content recommendation in video applications.

A method and apparatus comprising computer code configured to cause a processor or processors to receive a text comprising a plurality of sentences, by a machine learning model, extract a nonverbal message from one of the sentences and add an annotation to the text, the annotation indicating the nonverbal message, and output a version of the text including the annotation.

The invention discloses a handwritten comment extraction method and device, equipment and a storage medium, and belongs to the technical field of handwritten comment extraction, and the method comprises the steps: extracting a comment image in a drawing image; extracting multi-scale text features and symbol features of the handwritten annotation information, identifying a text annotation according to the text features, identifying a non-text annotation according to the symbol features, and splicing the text annotation and the non-text annotation to obtain electronic annotation information; establishing a coordinate conversion relationship between the drawing image and the electronic drawing; position coordinates of the electronic annotation information on the electronic drawing are determined according to the coordinate conversion relation; and embedding the electronic annotation information into the electronic drawing according to the position coordinates to obtain the electronic drawing with the annotation information. According to the method, the electronic annotation information is recognized through the text features and the symbol features, the position coordinates are determined according to the dynamic mapping relation between the drawing photo with the annotation content and the electronic drawing, and the electronic annotation content is embedded into the electronic document according to the position coordinates.

The invention discloses a speech recognition model training method and device, equipment and a readable storage medium, and relates to the technical field of artificial intelligence. Comprising the following steps: firstly, acquiring voice training data and annotation data corresponding to the voice training data; the annotation data comprises text annotation data and intention annotation data; fuzzy processing is carried out on the text labeling data, and voice features of the voice training data are extracted; and training a speech recognition model based on the speech features, the text annotation data and the intention annotation data until the speech recognition model converges. According to the method, the text annotation data is fuzzified, some characters which are not concerned about intention classification are ignored, the speech recognition model is more focused on keywords, intention classification information is introduced in the training process, and the accuracy of the recognition result generated by the speech recognition model for intention classification is improved.

This invention proposes a method and system for recommending English annotation methods with low forgetting rate based on the Analytic Hierarchy Process (AHP), belonging to the fields of optimization and intelligent decision theory. First, from the perspective of English learners, this invention analyzes and identifies five key factors affecting the rate of forgetting English vocabulary. Then, using the Analytic Hierarchy Process (AHP), a weight matrix that passes consistency detection is constructed for each influencing factor, and the actual influencing factor values ​​are normalized based on fuzzy logic. Based on the weight matrix that passes consistency detection and the normalized influencing factor scores under different text annotation methods, the estimated forgetting rate of English vocabulary for English learners under the corresponding annotation methods is obtained. The annotation method with the lowest estimated forgetting rate is selected as the optimal recommended annotation method to assist English learners in vocabulary learning. Finally, a one-month experimental verification of the proposed recommendation algorithm is conducted, and the experimental results demonstrate the effectiveness of this invention.

The present invention discloses a method and a marking system for scenic text annotation of bloggers' live video AR glasses. The method specifically includes: dynamically adjusting the knowledge graph retrieval strategy of the tourism live broadcast scene through the hot topic set and the sentiment classification result to obtain a dynamic retrieval result; obtaining the blogger's head pose data based on the IMU unit of the AR glasses, combining the geographical positioning information, and constructing a 3D spatial topology map of the tourism live broadcast scene through the SLAM algorithm; performing spatial registration on the dynamic retrieval result and the 3D spatial topology map, and combining to generate a scene enhancement annotation layer through the NeRF algorithm; superimposing and displaying the video stream and the scene enhancement annotation layer, and dynamically adjusting the annotation visibility by using an adaptive transparency algorithm. The present invention realizes the dynamic annotation of the scenic text content of the AR glasses by collecting the video stream and the interactive data stream in real time, can timely respond to the changes in the live broadcast scene and the interactive needs of the audience, and improves the real-time performance and accuracy of the live broadcast annotation.

The application provides a multi-screenshot editing and annotation system and a use method thereof, which comprises: a screenshot module, which realizes execution area screenshot, full-screen screenshot or window screenshot operation; an interface control module, which realizes dynamic adjustment of main interface size and menu layout according to screenshot size, controls interface top edge display and shelter area avoidance; a puzzle and overlay module, which realizes zooming, rotating, mirroring or transparency transformation of screenshots and arranges them in an automatic puzzle or overlay mode; a graphic editing module, which realizes addition / modification of graphic elements, realizes picture comparison and CAD auxiliary design function; a graphic and text annotation module, which realizes addition of graphic symbol marks, text annotation and mechanical drawing standard annotation; and a fast picture file management module, which realizes automatic saving of screenshots according to serial numbers, quick browsing and modification and user self-defined setting management. The application provides a more efficient and convenient multi-screenshot editing and annotation system and method, so as to simplify operation process, improve work efficiency and enhance user experience.

The invention relates to the technical field of text labeling, in particular to an intelligent labeling and model training inference system oriented to long text multi-dimensional evaluation, which realizes comprehensive and objective evaluation of long text quality by designing four general labeling dimensions (theme, structure, grammar and readable) and corresponding feature sets thereof. The system comprises a feature extraction module, an intelligent score estimation module, a manual annotation module, a model prediction module and an annotation processing module, feature extraction and score estimation can be efficiently completed, and the accuracy and stability of an annotation result are improved through manual correction. Compared with an existing labeling method depending on subjective judgment, the method has the advantages that the problems of labeling subjectivity and inconsistency are remarkably reduced, labeling efficiency and result objectivity are improved, support is provided for rapid backflow, data cleaning and incremental training of long texts, and the method is suitable for large-scale popularization and application. Therefore, the technical problem that quality is unstable due to the fact that the labeling process depends on subjective judgment is solved.

The present invention provides a method, device, equipment and storage medium for processing a speech recognition model. The method includes: responding to a model selection operation on a graphical user interface of an RPA platform, obtaining speech recognition models of multiple target scenes from a database of the RPA platform, iterating N times on the model parameters of the speech recognition models of the multiple target scenes to determine the model parameters of a federated speech recognition model, obtaining a final federated speech recognition model, and storing its model parameters in a preset path. The speech recognition model of each target scene is obtained by training a basic speech recognition model through transfer learning based on an audio sample of the target scene and a text annotation corresponding to the audio sample, and N is a positive integer. The federated speech recognition model obtained by the above scheme integrates the model parameters of multiple target scenes, improves the breadth and depth of recognition of the speech recognition model, and has a better recognition effect.

The present invention discloses a policy text annotation method and system combining pre-training and graph neural networks; wherein the method comprises: obtaining the policy text to be annotated, and pre-processing the policy text to be annotated; inputting the pre-processed policy text into a trained policy text annotation model, and outputting the annotation results of the policy text; wherein the working principle of the trained policy text annotation model comprises: extracting word vectors and sentence vectors for the processed policy text; constructing a text-level graph structure based on the pre-processed policy text, and obtaining an adjacency matrix corresponding to the text-level graph structure; extracting semantic features of the policy text based on the word vectors and sentence vectors; extracting structural features of the policy text based on the word vectors and the adjacency matrix; and determining the policy text annotation results based on the semantic features and structural features.

A speech recognition keyword enhancement method based on context associated words comprises the following steps: acquiring a speech data set and a text label corresponding to the speech data set, extracting keywords and keyword contexts to construct a dynamic word list, and performing feature extraction and data set division on speech data; a speech recognition model is constructed, the speech recognition model comprises a hybrid expert encoder and a double-path attention fusion mechanism, the hybrid expert encoder is used for carrying out a speech recognition task, and feature coding is carried out on keywords and keyword contexts through a keyword expert network and a context expert network which are connected in parallel; the double-path attention fusion mechanism carries out attention interaction on the voice features and the keyword coding features and the context coding features; performing end-to-end training on the model by jointly optimizing the loss function of the speech recognition task and the loss function of the keyword enhancement task; and performing keyword-enhanced real-time recognition on the input voice by using the trained model. According to the method, the recognition accuracy of the keywords in speech recognition can be improved.