Abstract: An image processing system and an image processing method for localising recognised characters in an image. An estimation unit is configured to estimate a first location of a recognised character that has been obtained by performing character recognition of the image. A determination unit is configured to determine second locations of a plurality of connected components in the image. A comparison unit is configured to compare the first location and the second locations, to identify a connected component associated with the recognised character. An association unit is configured to associate the recognised character, the identified connected component, and the second location of the identified connected component.

Abstract: An image processing system and an image processing method for localising recognised characters in an image. An estimation unit is configured to estimate a first location of a recognised character that has been obtained by performing character recognition of the image. A determination unit is configured to determine second locations of a plurality of connected components in the image. A comparison unit is configured to compare the first location and the second locations, to identify a connected component associated with the recognised character. An association unit is configured to associate the recognised character, the identified connected component, and the second location of the identified connected component.

Abstract: An image processing system and an image processing method for localising recognised characters in an image. An estimation unit is configured to estimate a first location of a recognised character that has been obtained by performing character recognition of the image. A determination unit is configured to determine second locations of a plurality of connected components in the image. A comparison unit is configured to compare the first location and the second locations, to identify a connected component associated with the recognised character. An association unit is configured to associate the recognised character, the identified connected component, and the second location of the identified connected component.

Abstract: A computer implemented method for optical character recognition (OCR) of a character string in a text image. The method efficiently combines two different OCR engines with the computation that needs to be done by the second OCR engine depending on the results found by the first OCR engine. This method provides, in particular, a high speed and accurate results when the first OCR engine is fast and the second OCR engine is accurate. The combination is possible because the second OCR engine identifies each segment to be processed by the second OCR engine without needing to process all segments.

Abstract: Processing a dithered image comprising a grid of pixels including defining an array of pixels corresponding to a sub-region of the image; performing edge detection along the rows and the columns of the array; counting the number of edges detected along the rows of the array to determine the number of horizontal edges in the array; counting the number of edges detected along the columns of the array to determine the number of vertical edges in the array; identifying whether the sub-region is dithered based on the number of horizontal and vertical edges in the array; and selectively processing the corresponding sub-region of the image based on whether or not the sub-region is identified to be dithered. The identification step may also be based on the lengths of segments of similar pixels in the lines of the array.

Abstract: An image processing method for recognising characters included in an image. A first character recognition unit performs recognition of a first group of characters corresponding to a first region of the image. A measuring unit calculates a confidence measure of the first group of characters. A determination unit determines whether further recognition is to be performed based on the confidence measure. A selection unit selects a second region of the image that includes the first region, if it is determined that further recognition is to be performed. A second character recognition unit performs further recognition of a second group of characters corresponding to the second region of the image.

Abstract: An image processing system and an image processing method for localising recognised characters in an image. An estimation unit is configured to estimate a first location of a recognised character that has been obtained by performing character recognition of the image. A determination unit is configured to determine second locations of a plurality of connected components in the image. A comparison unit is configured to compare the first location and the second locations, to identify a connected component associated with the recognised character. An association unit is configured to associate the recognised character, the identified connected component, and the second location of the identified connected component.

Abstract: Processing a dithered image comprising a grid of pixels including defining an array of pixels corresponding to a sub-region of the image; performing edge detection along the rows and the columns of the array; counting the number of edges detected along the rows of the array to determine the number of horizontal edges in the array; counting the number of edges detected along the columns of the array to determine the number of vertical edges in the array; identifying whether the sub-region is dithered based on the number of horizontal and vertical edges in the array; and selectively processing the corresponding sub-region of the image based on whether or not the sub-region is identified to be dithered. The identification step may also be based on the lengths of segments of similar pixels in the lines of the array.

Abstract: An image processing method for recognising characters included in an image. A first character recognition unit performs recognition of a first group of characters corresponding to a first region of the image. A measuring unit calculates a confidence measure of the first group of characters. A determination unit determines whether further recognition is to be performed based on the confidence measure. A selection unit selects a second region of the image that includes the first region, if it is determined that further recognition is to be performed. A second character recognition unit performs further recognition of a second group of characters corresponding to the second region of the image.

Abstract: A comprising: processing the image to a text image with a number of text blobs; classifying the text blobs based on a calculation as to whether they will belong to a foreground layer or to a background layer in OCR processing; and generating a quality value of the image based on the classified text blobs. By generating the quality value based on the classified text blobs, pictures in the image, which are not relevant for OCR are not taken into account for assessing the quality of the image. The amount of data to be processed is thereby decreased resulting in a method which can be executed in real-time. Furthermore, as the quality assessment criterion is based on the division of blobs into a foreground and a background layer, i.e. on prior knowledge of the OCR system, it provides a good indication for OCR accuracy.

Abstract: A computer implemented method for optical character recognition (OCR) of a character string in a text image. The method efficiently combines two different OCR engines with the computation that needs to be done by the second OCR engine depending on the results found by the first OCR engine. This method provides, in particular, a high speed and accurate results when the first OCR engine is fast and the second OCR engine is accurate. The combination is possible because the second OCR engine identifies each segment to be processed by the second OCR engine without needing to process all segments.

Abstract: A comprising: processing the image to a text image with a number of text blobs; classifying the text blobs based on a calculation as to whether they will belong to a foreground layer or to a background layer in OCR processing; and generating a quality value of the image based on the classified text blobs. By generating the quality value based on the classified text blobs, pictures in the image, which are not relevant for OCR are not taken into account for assessing the quality of the image. The amount of data to be processed is thereby decreased resulting in a method which can be executed in real-time. Furthermore, as the quality assessment criterion is based on the division of blobs into a foreground and a background layer, i.e. on prior knowledge of the OCR system, it provides a good indication for OCR accuracy.

Abstract: The invention relates to a method for combining a first Optical Character Recognition (OCR) and a second OCR. The first OCR is run first on an image of string of characters. Its output (first identified characters, positions of the characters and likelihood parameters of the characters) is used to generate a first graph. Segmentation points related to the positions of the first identified characters are used as input by the second OCR performing a combined segmentation and classification on the image of string of characters. The output (second identified characters, positions of the characters and likelihood parameters of the characters) of the second OCR is used to update the first graph to generate a second graph that combines the output of the first OCR with the output of the second OCR. Decision models are then used to modify the weights of paths in the second graph to generate a third graph.

Abstract: The optical character recognition method applies a first OCR engine to provide an identification of characters of at least a first type of characters and zones of at least a second type of characters in the character string image. A second OCR engine is applied on the zones of the at least second type of characters to provide an identification of characters of a second type of characters. The characters identified by the first OCR engine and by the second OCR engine are in a further step combined to obtain the identification of the characters of the character string image.

Abstract: A method for correcting an image acquired by a hand-held scanning device. A binarized image of an acquired image is cropped by removing columns on the left end and on the right end of only first components. A work image is created from the cropped image by replacing in each row of components series of first components smaller than a predetermined distance with series of second components. In the work image, a central line is identified. The identified central line in the work image is used to identify the corresponding central line in the cropped image forming a central line image, and in the central line image, the central text line is straightened.

Abstract: A method implemented on a mobile device for selecting information on a paper document using a hand-held scanning device. The method provides an interface to the user, receives information from the hand-held pen scanning device connected to the mobile device and determines if the received information is valid information for the type of information to be selected. The invention further relates to using a remote computer for performing image processing and data extraction when more powerful resources are needed.

Abstract: The invention relates to a method for combining a first Optical Character Recognition (OCR) and a second OCR. The first OCR is run first on an image of string of characters. Its output (first identified characters, positions of the characters and likelihood parameters of the characters) is used to generate a first graph. Segmentation points related to the positions of the first identified characters are used as input by the second OCR performing a combined segmentation and classification on the image of string of characters. The output (second identified characters, positions of the characters and likelihood parameters of the characters) of the second OCR is used to update the first graph to generate a second graph that combines the output of the first OCR with the output of the second OCR. Decision models are then used to modify the weights of paths in the second graph to generate a third graph.

Abstract: A method implemented on a mobile device for selecting information on a paper document using a hand-held scanning device. The method provides an interface to the user, receives information from the hand-held pen scanning device connected to the mobile device and determines if the received information is valid information for the type of information to be selected. The invention further relates to using a remote computer for performing image processing and data extraction when more powerful resources are needed.

Abstract: A method for correcting an image acquired by a hand-held scanning device. A binarized image of an acquired image is cropped by removing columns on the left end and on the right end of only first components. A work image is created from the cropped image by replacing in each row of components series of first components smaller than a predetermined distance with series of second components. In the work image, a central line is identified. The identified central line in the work image is used to identify the corresponding central line in the cropped image forming a central line image, and in the central line image, the central text line is straightened.

Abstract: A method for identifying a pattern in an image. In a first step the image is normalized to a binary matrix. A binary vector is subsequently generated from the binary matrix. The binary vector is filtered with a sparse matrix to a feature vector using a matrix vector multiplication wherein the matrix vector multiplication determines the values of the feature vector by applying program steps which are the result of transforming the sparse matrix in program steps including conditions on the values of the binary vector. Lastly, from the feature vector, a density of probability for a predetermined list of models is generated to identify the pattern in the image.