11 results about "Font recognition" patented technology

The invention provides a cross-font Chinese character recognition method based on structure embedding alignment, which aims at solving the problems of large stroke form change and strong structure variation of Chinese characters under different font styles, and comprises the following steps of: firstly, carrying out skeleton extraction on a character image and modeling into a topological graph structure with geometric attributes; extracting character graph embedding representation through a graph converter network comprising graph convolution, graph attention and a graph compression module; meanwhile, a structure tree corresponding to characters is constructed, a structure vector is generated by utilizing a structure embedding model, graph representation is supervised through structure consistency loss, and model learning is guided to have embedding features with structure stability and semantic consistency. According to the method, the recognition capability of the model for complex fonts, structural shielding and fuzzy characters is effectively improved, the structural understanding and generalization performance in a cross-font recognition task are remarkably enhanced, the problem that the recognition accuracy is reduced when font differences are large in an existing method is solved, and the method is suitable for various character image processing scenes.

The invention relates to the technical field of data processing, in particular to a handwritten font recognition and storage method for cold data archiving management. The method comprises the following steps: dividing a single font image into an intrinsic structure main body area and a style induction offset area based on the number of effective pixel points in each projection direction; determining a style influence degree according to the main body area and the offset area; clustering the single font images according to the style influence degree to form a cluster; according to the number of the single-font images in the clustering cluster and the style influence degree numerical range, the aggregation degree of the clustering cluster is determined; determining font structure ambiguity according to aggregation degree difference characteristics among the clusters; determining the overall font ambiguity by combining the font structure ambiguity; inputting the text image and the overall font ambiguity into a pre-training style correction model, and outputting a corrected text image; according to the method, the OCR algorithm is adopted to recognize and correct the handwritten fonts in the text image and store the handwritten fonts, so that the false recognition rate of handwritten font recognition is effectively reduced.

The present disclosure relates to a font recognition method, device, readable medium and electronic equipment. The font recognition method divides the image to be recognized into a plurality of sub-images through the predetermined font recognition model, and obtains the first image corresponding to each sub-image. Image features, determine the second image features corresponding to the image to be recognized based on the first image features corresponding to each sub-image of the image to be recognized, the second image features include each sub-image of the image to be recognized and the context-related features of other sub-images are used to determine the font type corresponding to the target text based on the second image features. In this way, the image to be recognized can be described more comprehensively and accurately based on the correlation between each word image and other sub-images. This can effectively improve the accuracy of font recognition results and also effectively improve the font recognition rate.

The invention discloses a handwritten font segmentation recognition method and system for a complex image background and a terminal, and the method comprises the steps: obtaining a target image from an image collection device of an intelligent terminal, carrying out the denoising and contrast adjustment of the target image, and obtaining a to-be-recognized field; according to the method, field category division, feature calculation according to field categories and font recognition according to features corresponding to the field categories, a locally deployed lightweight model or a cloud deployed high-performance model is intelligently selected for execution according to different preset mode switching rules, feature interference among different languages is reduced, and the font recognition efficiency is improved. The method improves the recognition accuracy, reduces the occupation of a local memory, improves the recognition speed in a complex scene, and can meet the recognition requirements of multi-language mixed handwritten fonts in an actual scene.

The invention provides a font recognition method, device and equipment based on self-adaptive preprocessing and a medium, and the method comprises the steps: carrying out the self-adaptive preprocessing of a collected font image, the preprocessing comprises dynamic noise suppression, illumination unevenness correction and sub-pixel-level geometric correction, and obtaining a corrected character image; performing multi-dimensional feature extraction on the corrected character image, and constructing a fusion feature vector including local detail features, global style features and geometric correction features; and performing dynamic weight matching based on the feature contribution degree evaluation model, calculating the similarity between the to-be-matched character and the candidate fonts, and outputting the recognized font type according to the similarity result, thereby ensuring the font recognition accuracy.

The application provides an image-based font recognition method and device, electronic equipment and storage medium. According to the embodiment of the application, first, the interference text elements outside the target text element are determined from the to-be-detected image, then the text relevance recognition result of the target image region outside the interference text element is obtained, which is used to represent at least one text paragraph region corresponding to the division of the target image region, and finally the target font corresponding to the target text element is obtained according to the font recognition result corresponding to the at least one text paragraph region. The above scheme can exclude the interference text element region in the to-be-detected image, so that the font recognition focuses on the target text element region in the to-be-detected image, reduces the calculation amount of font recognition, and further improves the efficiency of font recognition, which can adapt to the demand of image batch detection.

The invention discloses a contact picture automatic compliance auditing method and system. The method comprises the steps that a contact picture is acquired and preprocessed; matching a target template and a rule; executing multi-task recognition such as OCR, font recognition, layout analysis and the like, and outputting the confidence coefficient of each sub-module; calculating the comprehensive confidence of the field based on weighted fusion; outputting compliance / to-be-rechecked / non-compliance judgment according to comparison between the threshold group and the rule; generating a tamper-resistant evidence packet containing picture hash, a model version and a timestamp; when the template matching fails or the short-time violation sample exceeds the limit, generating a new template suggestion through clustering; and returning an artificial recheck result to the optimization model and the weight. The system correspondingly realizes the modular architecture of the functions. According to the method, the problems of low traditional auditing efficiency, high misjudgment rate and evidence chain deficiency are solved, and the automatic auditing precision and legal potency are remarkably improved.

The application provides a font detection method, device and equipment and medium. The font detection method comprises: determining a region image containing characters and the characters in the region image on a to-be-detected image; performing image classification on the region image to obtain a candidate font corresponding to the region image; performing target segmentation on the region image to obtain a character image corresponding to the region image; and determining a target font to which the characters in the region image belong according to the characters in the region image, the candidate font and the character image. Thus, in the font detection process, the user only needs to provide the to-be-detected image, and the font of the characters in the to-be-detected image can be obtained, the font detection efficiency is improved, the font recognition of batch images can be realized, and the accuracy of the font detection is effectively improved through the image classification, target segmentation and multiple stages.

The invention provides a subtitle resetting method. The subtitle resetting method comprises the following steps: carrying out text positioning of a video frame through text-visual sequence feature alignment fusion based on a converter architecture; performing font recognition based on character visual feature similarity measurement and matching on the positioned characters of the video frame; obtaining a new caption parameter defined by the vector profile by minimizing an energy function for coverage measurement based on the identified font; and obtaining vector subtitles after geometric transformation based on the new subtitle parameters so as to generate video frames comprising the vector subtitles. Therefore, accurate coverage of pixel-level character positioning is realized.

This invention relates to the field of character recognition technology, specifically to a method and system for recognizing Chinese characters in multiple fonts based on a pre-trained graph model. The method includes acquiring original character images and performing graph construction processing to generate structured character graphs; pre-training a graph neural network based on an unlabeled multi-font dataset by introducing a computational tree as a transferable local pattern, and constructing a discrete tree vocabulary using a vector quantization mechanism; wherein the graph neural network uses GraphSage as the backbone network; in the fine-tuning stage, the pre-trained graph encoder and tree vocabulary are loaded, and a residual mechanism is introduced to fuse the original graph embeddings and quantized graph embeddings, and a dedicated learning model for multi-font recognition tasks is obtained through fine-tuning on a target dataset; the dedicated learning model is used for multi-font generalization recognition; this invention effectively alleviates the intra-class discrepancy problem caused by font style differences and improves the model's recognition generalization ability in multi-font scenarios.

An English-Chinese massive font recognition method, system, medium and program product, in the method, a realistic font sample is generated; the realistic font sample is subjected to image enhancement processing to obtain a training font sample; the training font sample is labeled and divided into a training data set according to a preset proportion; a feature extraction network is constructed by using a deep separable convolution; the feature extraction network is trained by using the training data set; a font image to be recognized is input into the feature extraction network to obtain a plurality of scale feature maps; a discriminative score of the plurality of scale feature maps is calculated, the plurality of scale feature maps are weighted and fused according to the discriminative score, and a fused feature map is obtained; font classification is performed based on the fused feature map, and font category information and a similarity value corresponding to the font image to be recognized are obtained. The present application extracts multi-scale features by using a deep separable convolution and performs feature fusion based on a discriminative score, thereby improving the recognition accuracy of massive fonts.