Provided is information processing apparatus that extracts a handwriting comment. A comment acquiring part searches handwriting comment from an image data of a scanned manuscript and acquire the handwriting comment in association with position information indicating the handwriting comment for an area of the manuscript. A filing part converts the handwriting comment acquired by the comment acquiring part into a file. An OCR part performs optical character recognition (OCR). A filing part performs OCR of the comment by the OCR part, and when recognizable as a character, converts character data of the handwriting comment into the file, and when unrecognizable as a character, acquires the area of the handwriting comment from image data of the manuscript and converts into the file.

An information processing apparatus includes a processor configured to obtain, for each character of plural characters recognized from an image, (a) position of the character in the image, (b) size of the character, and (c) confidence level of a character recognition result of the character; and determine whether to regard the character as a noise based on a distance between the character and its nearest character, the size of the character, and the confidence level of the character recognition result of the character.

Implementations for optical character recognition of a language script can include actions of receiving an image comprising a graphical representation of a word written in the language script, segmenting the word into two or more segments, each segment being determined based on one or more of a variation in a height of the word and a variation in a width of the word, and including at least one character, providing a boundary for each segment of the two or more segments, the boundary enclosing the at least one character of a respective segment, each boundary having an edge with respect to an axis of the image, normalizing boundaries of the two or more segments by aligning edges of the boundaries, and labeling each segment of the two or more segments with a respective label, the respective label indicating a language character within the respective boundary.

A web detection system processes webpage information and performs automated feature extraction of webpages including machine processable information. In an embodiment, the web detection system determines a subset of webpages having a target characteristic by processing markup language. For a webpage of the subset, the web detection system determines that a first image overlaps at least a portion of a second image in the webpage. The web detection system generates an image of the webpage such that the portion of the second image is obscured by the first image. The web detection system determines a graphical feature of the webpage by processing the image, e.g., using optical character recognition. Responsive to determining that the graphical feature corresponds to graphical features of images of a different set of webpages associated with a target entity, the web detection system determines that the webpage is also associated with the target entity.

A system for determining compression rates for images, the system comprising a processing resource configured to: obtain a given image at least partially comprising a given text; compress the given image at a given compression ratio, giving rise to a compressed image; perform Optical Character Recognition (OCR) on the compressed image, giving rise to OCR text; compare the OCR text to the given text, giving rise to comparison results; upon the comparison results meeting a rule, increase the given compression rate; and upon the compression results not meeting a rule, return to a previous compression rate, if any.

Systems and methods for merging adjacent word fragments in outputs of optical character recognition (OCR) systems can include a processor obtaining word fragments associated with OCR data generated from an image. Each word fragment can be associated with a respective text line of a plurality of text lines. The at least one processor can determine, for each pair of adjacent word fragments in a text line, a respective normalized horizontal distance between the pair of adjacent word fragments. The processor can identify one or more pairs of adjacent word fragments that are candidates for merging based on the determined normalized horizontal distances. The processor can determine that a pair of adjacent word fragments, among the one or more pairs of adjacent word fragments that are candidates for merging, matches a predefined expression of a plurality of predefined expressions, and merge that pair of adjacent word fragments into a single word.

A method clusters each character on a document into one of a plurality of clusters based on widths of at least a portion of the characters on the document and measures distances between characters on the document. A threshold for each of the plurality of clusters is calculated based on at least a portion of the distances between characters in each cluster. The method then segments characters into units using the thresholds for the plurality of clusters. A distance between two characters in the document is compared to a threshold for a cluster to classify the two characters as being part of a unit when the distance is less than the threshold and not being part of the unit when the distance is greater than the threshold. Then, the method performs a recognition process on the document using the units.

In S210, a character size and an inter-column size are calculated using a virtual body size in S206, and the character size and an inter-character size (inter-column size) are reflected in properties of each text box. In step S301, a position and a size of each text box are set, in step S302, the character size is set in the property of each text box, and in the following step S304, the inter-character size is set in the property so that characters do not overlap in each text box. Thereafter, in S306, the text boxes are superimposed so that reference frames of adjacent characters are in contact with each other.

A web detection system processes webpage information and performs automated feature extraction of webpages including machine processable information. In an embodiment, the web detection system determines a subset of webpages having a target characteristic by processing markup language. For a webpage of the subset, the web detection system determines that a first image overlaps at least a portion of a second image in the webpage. The web detection system generates an image of the webpage such that the portion of the second image is obscured by the first image. The web detection system determines a graphical feature of the webpage by processing the image, e.g., using optical character recognition. Responsive to determining that the graphical feature corresponds to graphical features of images of a different set of webpages associated with a target entity, the web detection system determines that the webpage is also associated with the target entity.

An apparatus for character recognition executes a first process for acquiring first image data which is an image in which string data containing one or more characters is drawn at a first magnification through a drawing process that outputs image data acquired by drawing characters at a display magnification, executes a second process for acquiring second image data which is an image in which the string data is drawn at a second magnification larger than the first magnification through the drawing process, executes a third process for acquiring a recognition result including a character code of each of the characters in the string data drawn in the second image data through a character recognition process, and executes a fourth process for adjusting a newline position of the recognition result acquired from the second image data by using a newline position of the string data drawn in the first image data.