Abstract: Methods and an apparatuses for recognizing a text, recognition devices and storage media are provided, which belong to the field of text detections. A method includes: extracting, by the recognition device, a feature map of a to-be-recognized image, then determining segmentation information of a text region of the to-be-recognized image based on a preset segmentation network and the feature map, and then determining boundary key points in the text region based on the segmentation information, and then converting a text in the text region into a text with a target arrangement sequence based on the boundary key points and then inputting the text obtained by conversion into a preset recognition model for recognition processing.

Abstract: A sequence identification method and apparatus, an image processing device and a storage medium, which belong to the field of image identification, are provided. The method includes: performing feature extraction on a to-be-identified target image through an image identification model to obtain a first feature map, where the first feature map includes a plurality of first image features; performing time sequence relationship extraction on the first feature map based on a convolutional neural network layer and a fully connected layer in the image identification model, to obtain a second feature map that merges upper and lower information included in the to-be-identified target image, where the second feature map includes a plurality of second image features; and performing character identification on the to-be-identified target image in parallel based on the plurality of first image features and the plurality of second image features to obtain a character sequence.

Abstract: Methods and an apparatuses for recognizing a text, recognition devices and storage media are provided, which belong to the field of text detections. A method includes: extracting, by the recognition device, a feature map of a to-be-recognized image, then determining segmentation information of a text region of the to-be-recognized image based on a preset segmentation network and the feature map, and then determining boundary key points in the text region based on the segmentation information, and then converting a text in the text region into a text with a target arrangement sequence based on the boundary key points and then inputting the text obtained by conversion into a preset recognition model for recognition processing.