An incongruent headline detection system receives a request to determine a headline incongruence score for an electronic document. The incongruent headline detection system determines the headline incongruence score for the electronic document by applying a machine learning model to the electronic document. Applying the machine learning model to the electronic document includes generating a graph representing a textual similarity between a headline of the electronic document and each of a plurality of paragraphs of the electronic document and determining the headline incongruence score using the graph. The incongruent headline detection system transmits, responsive to the request, the headline incongruence score for the electronic document.

The present disclosure discloses a character offset detection method and system. The method includes: acquiring a text image; performing character separation based on the text image to obtain a character text region; calculating a center point of each rectangular box in the character text region to obtain a center point set; determining an optimal fitted curve based on the center point set; and analyzing character offset based on the optimal fitted curve to obtain an offset result. The present disclosure realizes detection of the character offset based on curve fitting, so that the accuracy of detection is improved.

A region of a drug to be identified is detected from a captured image generated by imaging the drug to be identified that is imparted with engraved mark and/or print. The region of the drug to be identified in the captured image is processed to acquire an engraved mark and print extraction image that is an extracted image of the engraved mark and/or print of the drug to be identified. The engraved mark and print extraction image is input, and a drug type of the drug to be identified is inferred to acquire a candidate of the drug type of the drug to be identified.

A method includes: obtaining an image of a keyboard layout; detecting, from the image, a plurality of key boundaries; determining a label string for a portion of the image defined by a corresponding key boundary from the plurality of key boundaries; selecting, for the determined label string, a corresponding key action; generating a keyboard layout definition for the image of the keyboard layout, the keyboard layout definition including a key definition for the corresponding key boundary, the key definition having: (i) a position for rendering of a key, (ii) a label configured to be rendered at the position of the key, and (iii) an action configured to be caused by selection of the key; and communicating the keyboard layout definition for deployment to a mobile device.

The present disclosure relates to a handwritten text recognition method, including: acquiring an information sequence including a plurality of track points of handwritten text, wherein information on each track point comprises its abscissa, writing time and writing state value; dividing the plurality of track points into a plurality of strokes according to the writing state value of each track point, the writing state value including a first value representative of stroke pen-up and a second value representative of stroke pen-down, respectively; calculating a first segmentation threshold of the handwritten text; determining a first text segmentation point according to a result of comparison between an absolute value of a difference between abscissas of a start track point of one stroke and an end track point of its previous stroke and the first segmentation threshold; and performing text segmentation according to the first text segmentation point to obtain a text segmentation result.

A handwritten content removing method and device and a storage medium. The handwritten content removing method comprises: acquiring an input image of a text page to be processed, the input image comprising a handwritten region, which comprises a handwritten content (S10); identifying the input image so as to determine the handwritten content in the handwritten region (S11); and removing the handwritten content in the input image so as to obtain an output image (S12).

This application discloses a data processing method and apparatus, a computer device, and a non-transitory computer-readable storage medium in the technical field of computers. This application, for textual data and picture data of an article, extracts a textual feature and a picture feature, respectively, and predicts an article classification to which the article belongs using a cross-modal interaction feature between the textual feature and picture feature. At the same time, this application considers the contribution degree of each of a textual modality and a picture modality to the article classification, rather than determining from a textual perspective only. In addition, the extracted cross-modal interaction feature is not a simple concatenation of the textual feature and the picture feature, which can reflect richer and deeper inter-modal interaction information, and greatly improve the identification accuracy of the article classification. Furthermore, it can improve the discovering accuracy of high-quality articles in the scene of identifying high-quality articles.

In one aspect, a computerized method for anti-counterfeiting solution using a machine learning (ML) model includes the step of providing a pre-defined set of feature detection rules, a pre-defined set of edge detection rules, a pre-defined threshold percentage, an original seal, an original fingerprint of the original seal, and a pre-trained fingerprint identification model. The pre-trained fingerprint identification model is trained by a specified ML algorithm using one or more digital images of the original seal. With a digital camera of a scanning device, the method scans a seal whose authenticity is to be determined. The seal is used to secure a transportation container. The method uses the pre-defined set of feature detection rules to detect and extract an extracted feature image at a specified position on the seal. The method breaks down the extracted feature image of the seal into a ‘kn’ number of sub-images by forming a ‘k’ rows x ‘n’ columns of a grid of the extracted feature image. The method implements the pre-defined set of edge detection rules to extract an edge structure of at least one object in each of the ‘kn’ number of sub-images. The method generates a set of unique fingerprints by specified steps. The method includes generating a unique fingerprint corresponding to a unique number or a feature based on each extracted edge structure. For the set of unique fingerprints, the method generates a match percentage for the set of unique fingerprints using the pre-trained fingerprint identification model. The match percentage corresponds to a matching proportion between each unique fingerprint generated for the seal being verified and the original fingerprint of the original seal on which the pre-trained fingerprint identification model is trained.

A method is provided for identifying improperly redacted information in documents. The documents are analyzed to detect redacted areas and text elements and to identify an intersection between a redacted area and a text element. When an area of the intersection is greater than an intersection threshold, the document is identified as containing improperly redacted information.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for developing cognitive and behavioral metrics associated with a user. In some implementations, a system obtains data from a writing implement, the data indicative of a user performing a task by writing with the writing implement against a receiving device. The system extracts features from the obtained data. The system determines metrics that reflect characteristics of the user, the metrics that reflect cognitive characteristics of the user and metrics that reflect behavioral characteristics of the user. Based on the extracted features and the determined metrics, the system generates a user profile for the user. Based on the features indicating characteristics of the writing behavior of the user, the system modifies the generated user profile as a performance of the user changes over time. The system provides recommendations to improve the performance of the user.