Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed create and store a decision tree, the nodes of which include classifiers that each recognizes the symbol that corresponds to a symbol image. Input of a symbol image to the decision tree and processing of the symbol image through one or more nodes of the decision tree returns a symbol corresponding to the symbol image.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed carry out an initial processing step on one or more scanned images to identify a subset of the total number of symbols frequently used in the scanned document image or images. One or more lists of graphemes for the language of the text are then ordered in most-likely-occurring to least-likely-occurring order to facilitate a second optical-character-recognition step in which symbol images extracted from the one or more scanned-document images are associated with one or more graphemes most likely to correspond to the scanned symbol image.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed carry out an initial processing step on one or more scanned images to identify, for each symbol image within a scanned document, a set of graphemes that match, with high frequency, symbol patterns that, in turn, match the symbol image. The set of graphemes identified for a symbol image is associated with the symbol image as a set of candidate graphemes for the symbol image. The set of candidate graphemes are then used, in one or more subsequent steps, to associate each symbol image with a most likely corresponding symbol code.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed create and store a decision tree, the nodes of which include classifiers that each recognizes the symbol that corresponds to a symbol image. Input of a symbol image to the decision tree and processing of the symbol image through one or more nodes of the decision tree returns a symbol corresponding to the symbol image.

Abstract: The current document is directed to methods and systems for identifying Chinese, Japanese, Korean, or similar language symbols that correspond to symbol images in a scanned-document image or other text-containing image. In a first processing phase, each symbol image is associated with a set of candidate graphemes. In a second processing phase, each symbol image is evaluated with respect to the set of candidate graphemes identified for the symbol image during the first phase. As candidate graphemes are processed, the currently described methods and systems monitor progress towards identifying a matching grapheme and, when insufficient progress is observed, terminate processing of the candidate graphemes and identify the symbol image as a non-symbol-containing area of the scanned-document image or other text-containing image.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed carry out an initial processing step on one or more scanned images to identify a subset of the total number of symbols frequently used in the scanned document image or images. One or more lists of graphemes for the language of the text are then ordered in most-likely-occurring to least-likely-occurring order to facilitate a second optical-character-recognition step in which symbol images extracted from the one or more scanned-document images are associated with one or more graphemes most likely to correspond to the scanned symbol image.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed carry out an initial processing step on one or more scanned images to identify, for each symbol image within a scanned document, a set of graphemes that match, with high frequency, symbol patterns that, in turn, match the symbol image. The set of graphemes identified for a symbol image is associated with the symbol image as a set of candidate graphemes for the symbol image. The set of candidate graphemes are then used, in one or more subsequent steps, to associate each symbol image with a most likely corresponding symbol code.

Abstract: The current application is directed to a method and system for automatically determining the sense orientation of regions of scanned-document images that include symbols and characters of languages that are not written as simple sequential strings of alphabetic characters. In one implementation, the sense-orientation method and system to which the current application is directed employs a relatively small set of orientation-marker characters that occur frequently in printed text and that lack rotational symmetry.

Abstract: A method for detecting a junction in a received image of the line of text to update a junction list with descriptive data is provided. The method includes creating a color histogram based on a number of color pixels in the received image of the line of text and detecting, based at least in part on the received image of the line of text, a rung within the received image of the line of text. The method also includes identifying a horizontal position of the detected rung in the received image of the line of text and identifying a gateway on the color histogram, wherein the identified gateway is associated with the detected rung. The junction list is updated with data including a description of the identified gateway.

Abstract: Methods are described for determining an optimal path for creating a scheme for dividing a text line of Chinese, Japanese or Korean (CJK) characters into character cells prior to applying classifiers and recognizing characters. Gaps between characters are found as a window is moved down the text line. Finding gaps may involve finding 4-connected paths. A histogram is built based on distances from start of window to a respective gap. The window is moved to the end of each gap after each gap is found and distances measured. Process is repeated until window reaches the end of the text line and all gaps found. A linear division graph (LDG) is constructed according to detected gaps. Penalties for certain distances are applied. An optimum path is one with a minimal penalty sum and can be used as a scheme for dividing text lines into character cells.

Abstract: The current application is directed to a method and system for automatically determining the sense orientation of regions of scanned-document images that include symbols and characters of languages that are not written as simple sequential strings of alphabetic characters. In one implementation, the sense-orientation method and system to which the current application is directed employs a relatively small set of orientation-marker characters that occur frequently in printed text and that lack rotational symmetry.

Abstract: A method for detecting a junction in a received image of the line of text to update a junction list with descriptive data is provided. The method includes creating a color histogram based on a number of color pixels in the received image of the line of text and detecting, based at least in part on the received image of the line of text, a rung within the received image of the line of text. The method also includes identifying a horizontal position of the detected rung in the received image of the line of text and identifying a gateway on the color histogram, wherein the identified gateway is associated with the detected rung. The junction list is updated with data including a description of the identified gateway.

Abstract: Methods are described for determining an optimal path for creating a scheme for dividing a text line of Chinese, Japanese or Korean (CJK) characters into character cells prior to applying classifiers and recognizing characters. Gaps between characters are found as a window is moved down the text line. Finding gaps may involve finding 4-connected paths. A histogram is built based on distances from start of window to a respective gap. The window is moved to the end of each gap after each gap is found and distances measured. Process is repeated until window reaches the end of the text line and all gaps found. A linear division graph (LDG) is constructed according to detected gaps. Penalties for certain distances are applied. An optimum path is one with a minimal penalty sum and can be used as a scheme for dividing text lines into character cells.

Abstract: A method is described for creating a scheme for dividing a text line of Chinese, Japanese or Korean (CJK) characters into character cells prior to applying classifiers and recognizing individual characters. Gaps between characters are found as a window is moved down the length of a text line. A histogram is built based on distances from the start of the window to a respective gap as the window is moved. The window is moved to the end of each gap after each gap is found and distances measured. This is repeated until the window reaches the end of the text line. A linear division graph (LDG) is constructed according to the detected gaps. Penalties for certain distances are applied. An optimum path is one with a minimal penalty sum and can be used as a scheme for dividing a text line into character cells.

Abstract: A method is described for creating a scheme for dividing a text line of Chinese, Japanese or Korean (CJK) characters into character cells prior to applying classifiers and recognizing individual characters. Gaps between characters are found as a window is moved down the length of a text line. A histogram is built based on distances from the start of the window to a respective gap as the window is moved. The window is moved to the end of each gap after each gap is found and distances measured. This is repeated until the window reaches the end of the text line. A linear division graph (LDG) is constructed according to the detected gaps. Penalties for certain distances are applied. An optimum path is one with a minimal penalty sum and can be used as a scheme for dividing a text line into character cells.