An image processing apparatus includes processing circuitry. The processing circuitry identifies an outer edge of a document with respect to image data optically read from the document and fills an outside from the outer edge in the image data with a predetermined color.

A system for determining correction of handwriting Chinese characters is provided. Text features of sampled template Chinese characters are calculated and stored in a feature set database. Then the text features of a handwriting Chinese character to be tested is also calculated. Then the two text features are compared, if they are matched, it is considered that the handwriting Chinese character to be tested has a correct handwriting way for the Chinese character. The result is feedback to the user. Therefore, the faults of the writer can be indicated in time as the user writes a Chinese character. Therefore, it is helpful for learning Chinese. A learner can know whether the Chinese character written now is correct or wrong real time. As a result, learning efficiency is promoted quickly.

An embodiment text recognition device includes a character position recognizer configured to recognize individual characters in an image, and the character position recognizer is also configured to recognize a position of each of the individual characters, a correction processor configured to set a main region, the correction processor further being configured to perform one or both of correcting a slope of the main region and magnification calibration for at least one character recognized by the character position recognition part, and a text recognizer configured to perform text recognition in the main region corrected by the correction processor.

The disclosure includes a system and method for determining a first measure of blur value associated with a first portion of a document under test; determining a second measure of blur value associated with a second portion of the document under test; determining whether an inconsistency in a set measure of blur values associated with the document under test is present, wherein the set of measure of blur values associated with the document under test includes the first measure of blur value and the second measure of blur value; and modifying a likelihood that the document is accepted or rejected based on whether the inconsistency is absent or present, respectively

An example electronic apparatus includes a user interface device, a communication device, a processor, and a memory to store instructions executable by the processor. The processor is to execute the instructions to obtain an image regarding a document including handwritten text, distinguish a writer who writes the handwritten text based on feature information of the handwritten text that is read from the image, and generate a file regarding the document based on at least one of setting information of the writer or setting information of the handwritten text.

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.

A system and method for training a neural network is implemented for detecting at least one entity in a document to derive relevant inferences therefrom. The method describes obtaining at least one document. The at least one document is processed, via a detection module, to detect a widget entity. The detected widget entity is classified as active or inactive based on a detected state of the widget entity. The classified widget entity is modified into a corresponding machine-readable widget-entity based on the detected state. The at least one document is processed, via an extraction module, to detect a text entity in near vicinity of the classified widget entity. A training pair comprising the machine-readable widget entity and the corresponding text entity is generated. The neural network is trained using the generated training pair.

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 computer-implemented method including detecting respective one or more text boxes in each of multiple partial images of a text-bearing area. The method also can include determining respective one or more edge text boxes of the respective one or more text boxes in each of overlapping partial images of the multiple partial images, wherein each of the respective one or more edge text boxes comprise a respective incomplete text. The method additionally can include matching one or more pairs of corresponding edge text boxes from the respective one or more edge text boxes of two adjacent images of the overlapping partial images of the multiple partial images. The method also can include determining cross-image texts in the one or more pairs of the corresponding edge text boxes. The method further can include determining one or more entities in the text-bearing area based on entity texts of the cross-image texts and non-edge texts in respective one or more non-edge text boxes of the respective one or more text boxes in the multiple partial images. Other embodiments are described.

An information processing system includes circuitry. The circuitry acquires a captured image by capturing a document. The circuitry performs an analysis process using the captured image. The circuitry selects, for each of at least one setting item of a plurality of setting items relating to image processing to be performed on the captured image, at least one setting value from among configurable setting values as a candidate for a recommended setting. The circuitry performs image processing repeatedly on the captured image while changing setting values of the plurality of setting items with a setting value of the at least one setting item restricted to the at least one setting value selected as the candidate for the recommended setting. The circuitry determines recommended settings for the plurality of setting items relating to image processing to obtain an image suitable for character recognition.