A CNN of an image forming apparatus includes: an encoder which compresses, for each tile image obtained by dividing an image into specific size pieces, information of the tile image; a decoder which restores the information of the tile image compressed by the encoder; and a blank sheet determination portion which determines whether the tile image corresponds to a blank sheet image. A segmentation image generation portion uses, when the blank sheet determination portion determines the tile image as being the blank sheet image, the blank sheet image for an image of a part corresponding to the tile image in a segmentation image, and uses, when the blank sheet determination portion determines the tile image as not being the blank sheet image, an output image of the decoder for an image of a part corresponding to the tile image in the segmentation image.

Method, computer readable medium, and apparatus of recognizing character zone in a digital document. In an embodiment, the method includes classifying a segment of the digital document as including text, calculating at least one parameter value associated with the classified segment of the digital document, determining, based on the calculated at least one parameter value, a zonal parameter value, classifying the segment of the digital document as a handwritten text zone or as a printed text zone based on the determined zonal parameter value and a threshold value, the threshold value being based on a selection of an intersection of a handwritten text distribution profile and a printed text distribution profile, each of the handwritten text distribution profile and the printed text distribution profile being associated with a zonal parameter corresponding to the determined zonal parameter value, and generating, based on the classifying, a modified version of the digital document.

An image processing system performs tilt correction with respect to a document image having handwritten characters and typed letters mixed with each other. The image processing system separates the document image into an image with handwritten characters determined as handwritten characters and an image without handwritten characters not determined as handwritten characters, estimates a tilt angle of the image without handwritten characters, and corrects the document image on the basis of the tilt angle.

A computer-readable, non-transitory recording medium contains therein an image processing program. The image processing program is for generating learning data of a character detection model that at least detects, to recognize a character in a document contained in an image, a position of the character in the image, and configured to cause a computer to generate a cropped image by cropping the image, and adopt the cropped image not containing an image representing a split character as the learning data, instead of adopting the cropped image containing the image representing the split character as the learning data.

A machine translation of a document is created via a compilation of services by mapping textual content from an image to create a plurality of mapped locations correspondent to at least one object from the image, populating each of the mapped locations with at least one character indicative of the object, each character sharing at least one similar attribute, adding to the image the populated mapped locations, and highlighting at least a portion of the textual content in accordance with the populated at least one character. A compilation of services is provided for identifying, extracting, and assessing electronic images by using a layered approach that reduces time and improves reviewing of medical records and other kinds of documentation.

The present disclosure relates to a tag-based font recognition system that utilizes a multi-learning framework to develop and improve tag-based font recognition using deep learning neural networks. In particular, the tag-based font recognition system jointly trains a font tag recognition neural network with an implicit font classification attention model to generate font tag probability vectors that are enhanced by implicit font classification information. Indeed, the font recognition system weights the hidden layers of the font tag recognition neural network with implicit font information to improve the accuracy and predictability of the font tag recognition neural network, which results in improved retrieval of fonts in response to a font tag query. Accordingly, using the enhanced tag probability vectors, the tag-based font recognition system can accurately identify and recommend one or more fonts in response to a font tag query.

System receives historical text block, creates historical features for historical text block's historical text lines. System trains machine-learning model to cluster historical features into historical features clusters based on their similarities. System identifies historical features cluster as historical human text cluster. System classifies each historical text line for historical human text cluster as human text, and each historical text line for other historical features clusters as machine text. System receives text block, creates features for text block's text lines. System applies trained machine-learning model to cluster features into features clusters based on their similarities. System identifies features cluster as human text cluster. System classifies each text line for human text cluster as human text, and each text line for other features clusters as machine text. System applies human text analysis to each text line classified as human text and machine text analysis to each text line classified as machine text.

Various computing technologies for content analysis.

A display apparatus includes circuitry to receive hand drafted data input by an electronic pen; display, on a screen, a plurality of character string candidates converted in a recognition language from the hand drafted data; and display a converted character string converted from one of the plurality of character string candidates, selected by the electronic pen, into a target language associated with identification information of the electronic pen. The target language is different from the recognition language. In response to selection of the converted character string, the circuitry displays a plurality of character string candidates in the recognition language corresponding to the converted character string.

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.