718 results about "Text graph" patented technology

A method and apparatus for organizing and processing interconnected pieces of information (“nodes”) using a digital computer is disclosed. Each node has elements that may be text, images, audio, video, and other computer programs. A graph-based user interface presents the individual nodes in spatial arrangements that reflect the relationships among the nodes. User interaction indicating interest in a particular node results in an increase in the “activation” of that node. This leads to an increase in the size of the presentation of that node, as well as an increase in the size of the presentation of closely related nodes. The result is a unique user interaction paradigm that allows for intuitive traversal of complex collections of nodes.

The invention belongs to the technical field of image processing, and specifically relates to an English text detection method with a text direction correction function. The method comprises the following steps of: respectively carrying out maximally stable extremal region detection on each channel of an English text image so as to obtain candidate text regions; establishing a convolutional neuralnetwork model-based classifier, and filtering wrong candidate text regions so as to obtain preliminary text regions; grouping the preliminary text regions by utilizing a double-layer text grouping algorithm; and carrying out direction correction on the grouped preliminary text regions so as to obtain a corrected text. According to the method, an enhanced multichannel MSER model is utilized to obtain more refined text regions, a parallel SPP-CNN classifier is imported to better distinguish text regions and non-text regions, images with any sizes can be processed, and pool features can be extracted under multiple scales, so that more features can be known through multilayer space information of source images; and the method is capable of processing slightly inclined scene texts.

The invention discloses a multi-modal knowledge graph construction method, and relates to the knowledge engineering technology in the field of big data. The method is realized through the following technical scheme: firstly, extracting multi-modal data semantic features based on a multi-modal data feature representation model, constructing a pre-training model-based data feature extraction model for texts, images, audios, videos and the like, and respectively finishing single-modal data semantic feature extraction; secondly, projecting different types of data into the same vector space for representation on the basis of unsupervised graph, attribute graph, heterogeneous graph embedding and other modes, so as to realize cross-modal multi-modal knowledge representation; on the basis of the above work, two maps needing to be fused and aligned are converted into vector representation forms respectively, then based on the obtained multi-modal knowledge representation, the mapping relation of entity pairs between knowledge maps is learned according to priori alignment data, multi-modal knowledge fusion disambiguation is completed, decoding and mapping to corresponding nodes in the knowledge maps are completed, and a fused new atlas, entities and attributes thereof are generated.

The invention provides an image compression method and equipment, and a system. The method comprises: according to a preset image block size, segmenting a to-be-compressed image into a plurality of image blocks that are not overlapped mutually; according to the colors and/or texture features of all image blocks, determining types of all the image blocks; and on the basis of a mapping relation between a preconfigured image type and a compression rule as well as the types of all the image blocks, carrying out compression coding on all image blocks so as to complete compression of the to-be-compressed image. According to the method, the equipment, and the system, effective compression of a mixed image containing text graph information and natural image information can be realized.

The invention relates to the field of automatic abstracting, and discloses a graph model-based automatic abstracting method. According to the technical scheme, an LDA probability topic model is applied to measurement of semantic correlation between sentences and improvement of the measurement effect of sentence correlation; and an idea of topic correlation and position sensitivity of the sentences is provided, so that abstract generation is relatively reasonable and effective. The method comprises the following steps: firstly, obtaining topic probability distribution of a document and word probability distribution of the topic through training the LDA topic model, determining the topic probability distribution of the sentences and effectively converting a semantic similarity measurement between the sentences into a similarity measurement problem of the topic probability distribution of the sentences; with the sentences as nodes, building edges by referring tothe cosine similarity and according to the semantic similarity between the sentences and generating a text graph representing the document; calculating the topic correlation between the sentences according to the topic probability distribution of the sentences and the topic probability distribution of the document; and calculating the position sensitivity and the like of the sentences according to the positions of the sentences in the document.

The invention discloses a scene text recognition method based on sparse coding characteristics, and relates to computer vision and pattern recognition. The method includes the steps: inputting a natural scene text image to be recognized; by the aid of a multi-scale sliding window method, detecting and recognizing a window area in the image by a character classifier, for each character class, determining a large output area of the classifier as a candidate character area, determining a small output area as a background area, finding the candidate character area in the image, retaining the area with the largest output value of the classifier and the corresponding character class for the area with a large overlapping ratio by the aid of a non-maximum suppression method, and removing the repetitive and redundant candidate character area to obtain a character detection result; combining detected characters into a word or text line; outputting a scene text recognition result. Structural characteristics of the characters can be more effectively expressed and extracted, so that the recognition rate of a scene text is increased.

Textual image coding involves coding textual portions of an image. In an example embodiment, a textual block of an image is decomposed into multiple base colors and an index map, with the index map having index values that each reference a base color so as to represent the textual block. A set of neighbor index values are ascertained for a particular index of the index map. A context that matches the neighbor index values is generated from among multiple contexts. The matching context includes a set of symbols. At least one symbol-to-value mapping is determined based on the matching context and a symbol to which the particular index corresponds. The particular index is remapped to a particular value in accordance with the symbol-to-value mapping and the symbol to which the particular index corresponds.

The invention discloses an implicit discourse relation recognition method based on multi-granularity generated image enhancement representation, and provides a multi-granularity generated image and aneural network for enhancing argument vector representation by simulating an association strategy for the first time due to the problems of ambiguity, fuzziness and the like of texts. Corresponding images according to different granularities (sentence level and phrase level) of texts are introduced, which helps understand semantics of chapters. In order to better capture context information of a text image, text and image features are integrated according to the sequence information of the text .Important image-text information and interaction information are captured in an image-text vector sequence representation whole formed by splicing two arguments by utilizing a self-attention mechanism; therefore, argument vector representation is further enriched, feature vector representation usedfor recognizing the discourse relations is obtained, and finally the feature vector representation used for recognizing the discourse relations is input into the discourse relation recognition layerfor discourse relation recognition.

The invention discloses a electronic device that comprises: An image acquisition module, is used to acquire a text image to be input; a database module, which is a characteristic database at least with character sets and words to be identified; a central process module, according to the characteristic database that is stored in the database module, utilize a comparison identification algorithm to convert the text images that are acquired with the image acquisition module into the original text format. Accordingly, the invention also discloses a text input method for electronic devices. The invented electronic device and the relevant text input method can fulfill that, in a video-recording input mode, input the text into the electronic device and convert the text into a text format, so as to increase text input efficiency.

The embodiment of the invention provides a text detection method and device, electronic equipment and a computer storage medium, and the method comprises the steps: carrying out the feature extractionand segmentation of a to-be-detected text image, and obtaining a text region threshold graph and a text region central point probability graph of the to-be-detected text image; obtaining a text areaframe binary image corresponding to the text area threshold image and a text area center point binary image corresponding to the text area center point probability image; and carrying out connected domain detection on the text area center point binary image, determining a clustering center of the text area, and further determining a text detection result in the to-be-detected text image accordingto the similarity between pixel points of a text area frame in the text area frame binary image and the clustering center. Through the method of the invention, the speed of text detection, especiallydense text detection, is increased.

The embodiment of the invention discloses a text detection method, electronic equipment and a computer readable medium, and the text detection method comprises the steps: carrying out the feature extraction and image segmentation of a to-be-detected text image, and at least obtaining a text region probability graph of the to-be-detected text image and the image features of the to-be-detected textimage; binarizing the text region probability graph to obtain a text region binary graph; obtaining at least one text connected domain according to the text region binary image and the image features;obtaining a text region approximate boundary of at least one text connected domain; and obtaining a text detection result of the to-be-detected text image according to the text region approximate boundary and a preset boundary threshold. According to the embodiment of the invention, the speed and efficiency of text detection, especially dense text detection, are improved.

The invention relates to an American license plate recognition method and system based on image correction, and a text detection, image correction, text recognition and text classification module, andthe method comprises the following steps: carrying out the preprocessing of an image file of a data set, carrying out the data enhancement, and generating a training set and a test set; designing a text detection module, detecting text information in the image, realizing text and background segmentation in the image, and obtaining a text image only containing the text information; correcting thetext image by adopting an image correction module, and converting the originally distorted or inclined text image into a horizontal direction; recognizing the corrected text image to obtain letters, numbers and other information contained in the text image; and constructing a text classification module, screening out a license plate number, an Asian name and an annual inspection date from all textinformation, and completing license plate recognition. According to the invention, the problems of complex background pattern, fuzzy target text image deformation, complex text information and largecalculation amount when a neural network is used for off-line training during American license plate recognition are solved.

The invention relates to a method for tracing text and strengthening multi-frame in a video. The texts in the video are mostly laminated in a complicated background, and if the texts are directly delivered into OCR (Optical Character Recognition) software for recognition, the recognition rate is low, therefore, the text strengthening operations are required to separate the texts from the background. The texts in the video mostly last for tens of frames, and even hundreds of frames, and in the adjacent frames, the colour of the texts are basically invariable but the background changes dynamically, accordingly, abundant complementary information between the multi-frames can be used for strengthening the texts. The method of the invention eliminates the influence of background edge pixel through the text stroke's characteristic of possession of edge pair, then uses Hausdorff distance measurement method for tracing the location of the texts at the adjacent frame, after obtaining a plurality of copies of the text images at the adjacent frame, makes use of minimum pixel searching method to remove the background in order to acquire binary text image including the clean background, greatly improving the rate of OCR software in identifying the video texts.

The embodiment of the invention discloses a text detection method, electronic equipment and a computer readable medium, and the text detection method comprises the steps: carrying out the feature extraction and segmentation of a to-be-detected text image, and obtaining a text region probability graph of the to-be-detected text image; determining a text region binary image of the to-be-detected text image according to the text region probability graph; extracting edge information of the text area binary image to obtain a text area edge image; performing connected domain detection on the text area edge graph, and obtaining a minimum enclosing rectangle of the text area according to a detection result; and obtaining a text detection result of the to-be-detected text image according to the minimum bounding rectangle. According to the method of the invention, the speed and efficiency of text detection, especially dense text detection, are improved.

The invention discloses a natural scene text recognition method based on a cross attention mechanism, and the method comprises the steps: data obtaining: downloading a sample picture in a natural scene, and synthesizing the picture into a training set through employing a public code; wherein stretching operation is conducted on the sizes of all the training sample pictures, the size of the processed sample pictures is 32 * 100, the height-width ratio is kept consistent with that of an original picture, and the insufficient parts are filled with black edges; label manufacturing: a supervised method is adopted to train an identification model, so that each row of text pictures has corresponding text information; training a network: inputting the prepared training picture data and labels intoa cross attention network for training, wherein the cross attention network is composed of a vertical attention network and a horizontal attention network; inputting test data into the trained network, and finally obtaining an identification result and predicting the confidence coefficient of each character. The method is high in recognition accuracy and strong in robustness, and has good recognition performance for texts with irregular shapes.

The invention provides a text detection method and device, electronic equipment and a storage medium. According to the embodiments of the invention, the method comprises the following steps of: performing feature extraction on a text image to obtain a feature image; processing the feature image by using a convolutional neural network to obtain the probability that pixel points in the text image belong to a text region; processing the feature image by using a sequence model to obtain a key point position of the text region in the text image; and determining a text region detection result of thetext image according to the probability that the pixel point belongs to the text region and the key point position of the text region. According to the text detection method and device, the electronic equipment and the storage medium of the embodiment of the invention, the anti-interference capability of the text detection method can be improved while the text detection speed is increased, so that the robustness of the text detection method is stronger.

The embodiment of the invention discloses a text recognition method, electronic equipment and a computer readable medium. The text recognition method comprises the steps: performing feature extraction on a text image to be recognized, and obtaining corresponding image features; performing self-attention calculation processing based on the image features to obtain corresponding feature coding vectors; carrying out character position enhancement processing based on the image features to obtain corresponding character position coding vectors; splicing the feature coding vectors and the character position coding vectors, and performing semantic feature extraction on the spliced coding vector to obtain a semantic feature vector; and decoding the semantic feature vector to obtain a corresponding text character. According to the scheme of the embodiment of the invention, the character recognition accuracy and the character bit sequence of the finally decoded character are more accurate.

The invention provides a method for recognizing a natural scene text in any shape, and the method comprises the following steps: S1, collecting a natural scene text image, and making a training data set and a test data set; S2, constructing a text recognition network model, and optimizing network model parameters by using the labeled training sample data to obtain an optimal text recognition network model; and S3, inputting a to-be-recognized natural scene text image into the trained network model to obtain a text recognition result, and combining a spatial attention mechanism in text recognition to improve spatial position information for text recognition and improve the sensitivity of an attention mechanism module to the position information, so that the method has relatively high robustness to the appearance of a text, and text recognition of any shape can be realized. The method achieves a good effect on solving the problem of detection and recognition of text instances with extreme aspect ratios or irregular shapes, and the recognition precision of the method is not affected by nearby texts or background noise.