A method of detecting fields in document images includes: receiving a codebook comprising a set of visual words, each visual word corresponding to a center of a cluster of local descriptors; calculating, based on a set of user labeled document images, for each visual word of the codebook, a respective frequency distribution of a field position of a specified labeled field with respect to the visual word; loading a document image for extraction of target fields; calculating a statistical predicate of a possible position of a target field in the document image based on the frequency distributions; and detecting, using the trained model, fields in the document image based on the calculated statistical predicate.

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

A method of detecting fields in document images includes: receiving, by a processing device, a codebook comprising a set of visual words, each visual word corresponding to a center of a cluster of local descriptors, wherein each local descriptor is associated with a respective keypoint region of a first set of document images; calculating, based on a second set of document images, for each visual word of the codebook, a respective frequency distribution of a field position of a specified field with respect to the visual word; loading a document image for extraction of target fields; and detecting fields in the document image based on the calculated frequency distributions.

Provided is a work record extraction device that can correctly select a drawing element corresponding to handwriting even if there is a handwriting deviation when position coordinates are collated between handwritten data overwritten on drawing data by manual input and a drawing element on the drawing data. The work record extraction device according to the invention sets, around a drawing element, a boundary area including at least a part of the drawing element, determines whether handwritten data passes through at least a part of the boundary area, and determines that the handwritten data passes through the drawing element in a case where the handwritten data passes through at least a part of the boundary area.

Image processing techniques are described, including techniques in which text data associated with an image is used to determine a font of text in an image. The image is split into a plurality of crops based on the text data. A trained machine learning model is used to determine feature vectors of the image. The feature vectors are combined into a combined feature vector. A second trained machine learning model is used to determine a font using the combined feature vector. The second trained machine learning model may be a multi-layer perceptron network. The second trained machined learning model may be trained on a plurality of images with text of known fonts and properties. The described image processing techniques also include text removal.

A system and method of extracting tables and figures from a drawing document is disclosed. The method may include processing coloured image to segmented binary image and extracting a plurality of horizontal lines and a plurality of vertical lines from a foreground of the image. The method may further include detecting a set of candidate table region from the plurality of horizontal lines and the plurality of vertical lines in the image. Further, the method may include calculating textual region density corresponding to each of the set of candidate table regions in the image. The method may further include identifying at least one relevant table region from the set of candidate table regions in the image and a text free region from the at least one additional region in the image. The method may further include identifying at least one figure region from the dilated text free region.