System for analysing table images

Abstract: A method for block selection on a image of a table, the table including rows and columns defined by visible and non-visible grid lines and containing table cells, includes identifying super-cells that include one or more table cells, wherein super-cells are identified according to traced white areas surrounding table cells and bounded by visible grid lines, determining whether vertical and horizontal grid lines bounding each table cell are visible or non-visible, and determining whether vertical and horizontal grid lines bounding each super-cell are visible or non-visible.

Description: The present invention relates to page segmentation systems for classifying regions of a document image. More particularly, the present invention relates to a block selection system for identifying and defining features within table images. Conventional page segmentation systems are applied to document images in order to identify data types contained within specific regions of the document images. This information can be used to extract data within a specific region and to determine a type of processing to be applied to the extracted data. For a document containing a table image, a region of text, or table cell, located within the table image can be converted to ASCII characters using optical character recognition (OCR) processing and stored in an ASCII file along with information corresponding to the location of the table cell. However, conventional systems cannot accurately determine a row and column address corresponding to the table cell. Accordingly, the recognized ASCII characters cannot be reliably input to a spreadsheet based on row and column address data. In addition, the data produced by conventional systems is often insufficient to adequately recreate the internal features of a bit-mapped table image. For example, the data does not reflect vertical and horizontal grid lines within an analyzed table image. As defined herein, vertical and horizontal grid lines define each row and column within a table, and can be either visible or non-visible. Therefore, although a conventional system can be used to create an ASCII version of a bit-mapped table, the ASCII version does not include data representative of table grid lines. Accordingly, the stored data cannot be used to accurately recreate a bit-mapped version of grid lines within the table. Moreover, in a case that it is desired to edit text within a table cell, it is difficult to determine, based on information provided by conventional systems whether the edited text will intersect with a grid line and thereby violate row/column boundaries. Consequently, what is needed is a system for accurately identifying and representing internal features of a bit-mapped table image, such as rows, columns, and table grid lines. An aspect of the present invention identifies super-cells within a bit-mapped table image. Super-cells are areas of a table image bounded by visible table grid lines and which include one or more table cells. Advantageously, the locations and dimensions of the identified super-cells can be used to reconstruct a bit-mapped image of the visible grid lines of the table image. In addition, by referring to the dimensions of a super-cell surrounding a table cell, it is possible to determine whether editing text of the table cell will cause the edited text to intersect a grid line. According to a first aspect of the present invention, there is provided a method for analyzing a table image as defined in claim 1. Further aspects of the invention include a computer program comprising processor implementable instructions for performing all the steps of a method according to the first aspect of the invention. By virtue of the foregoing, data contained in table cells can be accurately extracted and output to a spreadsheet application. Moreover, additional cells can be easily added to the table based on existing rows and columns. Embodiments of the present invention will now be described with reference to the accompanying drawings, in which: Figure 1 is a view showing representative computing equipment embodying the present invention. Figure 2 is a block diagram illustrating the internal architecture of a computer embodying the present invention. Figure 3 is a flow diagram describing block selection processing according to an embodiment of the present invention. Figure 4 is a flow diagram describing classification of non-text connected components according to an embodiment of the present invention. Figure 5 is a flow diagram specifically directed to block selection processing of table images. Figure 6 is a view of a table having a reversed area. Figure 7 is a flow diagram describing processing of a reversed table area according to an embodiment of the present invention. Figure 8 is a view of a table portion for describing extraction of an attached connected component. Figure 9, comprising Figures 9a and 9b, are flow diagrams describing table column calculation according to an embodiment of the present invention. Figure 10, comprising Figures 10a and 10b, illustrates a vertical histogram used in table column calculation of an embodiment of the present invention. Figure 11, comprising Figures 11a through 11h, shows various histogram configurations considered during table column calculation according to an embodiments of the present invention. Figure 12, comprising Figures 12a to 12c, illustrates table column calculation according to an embodiment of the present invention. Figure 13 is a diagram illustrating the precession of a head at a precession angle . Figure 14 is a diagram illustrating the calculation of the angle of the precession. Figure 15 is a diagram illustrating the information of Figure 14 projecting onto a sphere. Figure 16 is a diagram illustrating the interception of the tool and the surface of the workpiece. Figure 17 is a flow diagram describing super-cell creation according to an embodiment of the present invention. Figure 18, comprising Figures 18a and 18b, illustrates super-cell creation according to an embodiment of the present invention. Figure 19, comprising Figure 19a to 19c, illustrates vertical histogram configurations considered during column splitting according to an embodiment of the present invention. Figure 20 is a view of a table portion for describing row splitting according to an embodiment of the present invention. Figure 21, comprising Figures 21a and 21b, is a flow diagram for describing table cell column and row splitting according to an embodiment of the present invention. Figure 22, comprising Figures 22a and 22b, illustrates column adjustment according to an embodiment of the present invention. Figure 23 shows a table portion for describing column address adjustment based on slanted grid lines. Figure 24 is a view of the Figure 23 table portion after column address adjustment according to an embodiment of the present invention. Figure 25 is a flow diagram describing creation of dummy super-cells according to an embodiment of the present invention. Figure 26, comprising Figures 26a and 26b, illustrates super-cell hole compensation according to an embodiment of the present invention. Figure 27 illustrates a dummy cell according to an embodiment of the present invention. Figure 28, comprising Figures 28a and 28b, illustrates relationships between table cells, super-cells, and visible grid lines. Figure 29, comprising Figures 29a and 29b, shows hierarchical tree structures for storing data representative of table images. Figure 1 is a view showing the outward appearance of representative computing equipment embodying a block selection-based table analysis system according to an embodiment of the present invention. Shown in Figure 1 is computing equipment 1, such as an IBM PC or PC-compatible computer having a windowing operating system such as the Microsoft Windows95 operating system. Computing equipment 1 is provided with either a monochromatic or color display monitor 2, using which computing equipment 1 displays images to a user. Computing equipment 1 is also provided with fixed disk drive 3 for storing data files and application program files, keyboard 5 for inputting text data and for manipulating objects displayed on display screen 2, and pointing device 6, such as a mouse, which is provided for pointing to and manipulating objects displayed on display screen 2. Computing system 1 also includes floppy disk drive 4 for reading from and writing to a floppy disk. Document image files including table images, as well as computer-executable process steps embodying the present invention, may be stored either on fixed disk 3 or on a floppy disk inserted in floppy disk drive 4. Alternatively, document image files and/or computer-executable process steps may be obtained from a CD-ROM accessed via a CD-ROM drive (not shown). Also provided with computing equipment 1 are network interface 10 for interfacing with a local area network, and facsimile/modem interface 11 for sending and receiving facsimile messages and other data files. Document image files and computer-executable process steps embodying the present invention may be accessed over a network via network interface 10. In addition, facsimile/modem interface 11 can also be used for retrieving images and/or computer-executable process steps from the World Wide Web (hereafter WWW). Most typically, the applications stored on fixed disk 3, including a block selection application are stored to disk 3 after being downloaded from a computer-readable medium, such as a floppy disk, a CD-ROM, a network drive, or the WWW. A document image may be input by scanner 7, which scans a document in order to provide bit-mapped image data of the document to computing equipment 1. The bit-mapped image data, which includes bit-mapped table image data, may be stored onto fixed disk 3 in either a compressed or an uncompressed format. Printer 9 is provided for outputting document images processed by computing equipment 1. In operation, and under control of the windowing operating system, stored application programs, such as a block selection application according to the present invention, are selectably activated to process and to manipulate stored data. In accordance with operator instructions, and based on the stored application programs, commands are issued to display images on screen 2, and to print the displayed images using printer 9. Figure 2 is a detailed block diagram showing the internal construction of computing equipment 1. As shown in Figure 2, computing equipment 1 includes a central processing unit (CPU) 20, such as a programmed microprocessor, interfaced to computer bus 21. Also interfaced to computer bus 21 are scanner interface 22, printer interface 23, network interface 24, fax/modem interface 26, display interface 27, keyboard interface 28, and mouse interface 29. Main memory 30, such as random access memory (RAM), interfaces to computer bus 21 so as to provide CPU 20 with access to memory storage. In particular, when executing computer-executable process steps such as those stored on disk 3, CPU 20 loads those steps from disk 3 or other storage media into main memory 30 and executes those process steps out of main memory 30. Read-only memory (ROM) 31 is used for storing computer-executable process steps, such as those used during boot-up, or basic input/output operating system (BIOS) sequences for operation of, for example, keyboard 5. Figure 3 is a flow diagram describing a method of block selection processing according to the present invention. The process steps shown in Figure 3 are preferably executed by CPU 20 in accordance with a block selection application stored on a computer-readable medium such as disk 3. Generally, the process steps of Figure 3 are used to input a document image, to detect connected components within the document image, to separate text and non-text connected components, to classify non-text connected components, to identify white areas along edges of non-text components, to form text block and text lines, to detect skew of the input image, and to post-process the image. Techniques for performing these general steps are disclosed in U.S. Patent Nos. 5,680,479 and 5,588,072. It should be noted that the process steps of Figure 3 are also used to identify reversed text areas within a table, which are text areas in which text is composed by white pixels surrounded by black pixels, to identify attached text connected components, to create and calculate addresses for table cells and super-cells, which are areas within a table region bounded by visible vertical and/or horizontal grid lines within the table and which contain at least one table cell, to check the existence of visible cell borders, and to determine the location of table grid lines according to the present invention. In this regard, grid lines within a table are either visible or non-visible and also define each row and column identified within the table. Specifically, in step S301 of Figure 3, bit-mapped pixel data of a document image is input into computing system 1 and is stored on disk 3. Preferably, the pixel data is binary pixel data, that is, black and white image data. However, in a case that the image data is halftone image data, in which pixels are represented by one of several grayscale levels, or in a case where the pixel data is color image data, in which each pixel is represented by a multi-bit word encoding color data for the pixel, then threshold processing should be performed so as to binarize the non-binary pixel data into binary pixel data. It should be noted that image reduction may be performed following step S301. Such image reduction increases the speed of subsequent processing, but may also have an adverse effect on feature-intensive processing, such as optical character recognition. In step S302, the input pixel data is analyzed so as to detect connected components within the document image. A connected component is a group of black pixels that is completely surrounded by white pixels. The detected connected components are rectangularized, or "blocked", by defining, for each connected component, a smallest rectangle circumscribing the connected component. In step S304, the connected components are roughly classified into text connected components and non-text connected components. This classification is based on the size of the rectangles circumscribing the connected components and on the fact that non-text connected components are usually larger than text connected components. Detailed descriptions of steps S301, S302, and S304 are located within U.S. Patent Nos. 5,588,072 and 5,680,479. In step S305, connected components classified as non-text in step S304 are further analyzed to determine whether they are lines, joint lines, pictures, line art, frames, tables, or unknown (i.e., none of the above). Figure 4 is a flow diagram describing a method for analyzing non-text connected components according to step S305. Because individual steps of Figure 4 are described in greater detail in U.S. Patent Nos. 5,588,072 and 5,680,479, only a general description will be provided below. Generally, the non-text connected component classification described in Figure 4 is based on criteria thresholds which are formulated mathematically and calculated dynamically based on the size, width and the like of the connected components being analyzed. Thus, in step S401, a non-text connected component is analyzed to determine whether it is a halftone (or continuous-tone) picture. If so, flow advances to step S402, at which point a "halftone (continuous-tone) picture" sub-attribute is assigned to the non-text connected component. If the non-text connected component is not determined to be a halftone picture in step S401, then flow advances to step S404 in which it is determined whether the non-text connected component is a frame. If so, flow advances to step S405 at which time a "frame" sub-attribute is assigned to the connected component. If the determination in step S404 is negative, flow proceeds to step S406, wherein it is determined whether the non-text connected component is a horizontal or vertical line. Flow proceeds to step S407 in a case that the connected component is determined to be a horizontal or vertical line. In step S407, a "horizontal line" or "vertical line" sub-attribute is assigned to the non-text connected component. Step S409 is executed in a case that, in step S406, the non-text connected component is not determined to be a vertical line or a horizontal line. In step S409, it is determined whether the non-text connected component is a table. Briefly, in order to determine whether the non-text connected component is a table, internal white areas of the connected component are traced in four directions. Internal white areas are areas of white pixels completely surrounded by black pixels. If four internal white areas are found, and if the arrangement of the white areas is in a table-like grid such that the white areas are enclosed by horizontal and vertical lines, then the non-text connected component is designated as a table in step S410. A more detailed description for determining whether a connected component is a table is found in U.S. Patent No. 5,588,072, U.S. Patent No. 5,680,479, and U.S. Patent Application No. 08/514,252. Also in step S410, the interior of the identified table is re-analyzed in accordance with steps S302 and S304 so as to identify and classify text and non-text connected components internal to the table. If it was not determined in step S409 that the non-text connected component is a table, flow advances to step S412 in which it is determined whether the non-text connected component is a slanted line. If so, a "slant line" sub-attribute is assigned to the non-text connected component in step S414. If the determination in step S412 is negative, flow proceeds to step S415, where it is determined if the non-text connected component is a line-art picture or a joint line. If the non-text connected component is not a line-art picture or a joint line, flow proceeds to step S416, wherein an "unknown" attribute is assigned to the connected component. If so, flow proceeds to step S417, wherein it is determined whether the non-text connected component is a joint line. If the non-text connected component is not a joint line, then the non-text connected component is, in step S419, designated as a line art picture. If the non-text connected component is determined to be a joint-line picture in step S417, flow proceeds to step S420, where a "joint line" sub-attribute is assigned to the component. Flow then proceeds to step S421. It should be noted that each of steps S402, S405, S407, S410, S414, S416, and S419 also flow to step S421, wherein it is determined whether additional non-text connected components remain to be classified. If so, flow returns to step S401. If not, flow proceeds to step S306 of Figure 3. In step S306, invisible lines are identified along the edge of non-text connected components. As mentioned above, invisible line identification is disclosed in detail in U.S. Patent Nos. 5,588,072 and 5,680,479. For example, a vertical invisible line, or region of white pixels, may be located in step S306 between two halftone picture connected components. This invisible line can be used in step S308 to determine whether text connected components located below the picture connected components should be grouped together or in separate columnar blocks. Flow proceeds from step S306 to step S307, in which reversed areas within table regions are identified. A reversed area is a region including text composed of white pixels surrounded by black pixels. Reversed areas within table regions are ignored in subsequent processing so as not to interfere with identification of other table features. A detailed description of reversed area identification is given below with respect to Figures 6 and 7. Also in step S307, connected components which are attached to horizontal and vertical grid lines within table regions are identified. Identification and extraction of attached connected components are described generally below with respect to Figure 8 and in detail in commonly-assigned U.S. Patent Application No. 08/664,674. As also described below, identified attached components are used to identify other features within a table. Flow continues to step S308, in which text blocks are formed from the text connected components classified in step S304. In this regard, close horizontal and vertical neighbours of each text connected component are aggregated into text blocks based on a statistical analysis of horizontal and vertical gaps between neighbouring text units. Next, the formed text blocks are analyzed so as to form text lines. Step S308 is described in detail in U.S. Patent No. 5,588,072. Next, in step S309, process steps are executed in accordance with the present invention so as to analyze non-text connected components which have been designated as tables. More specifically, rows and columns within the tables are identified and row and column addresses are assigned to each table cell within the tables. Row and column address identification and assignment are described in detail below with respect to Figures 9 to 14. In addition, locations of vertical and horizontal table grid lines are calculated in step S309, the grid lines defining each identified row and column and being either visible and non-visible. A description of grid line calculation is given below with respect to Figures 15 and 16. Super-cells are also defined in step S309 based on table areas bounded by visible grid lines and containing at least one table cell. Row and column addresses are assigned to the super-cells, and visible grid line borders are identified surrounding each super-cell. Creation of super-cells and identification of visible borders are described with respect to Figures 17 and 18 hereinbelow. Next, in step S310, the skew of the input document image is detected and, if the skew angle exceeds a predefined maximum angle, an error code is output, signalling to the user that the document page is too skewed to complete the block selection process. Skew detection is disclosed in U.S. Patent Nos. 5,588,072 and 5,680,479. If the skew is within acceptable limits, flow advances to step S311, in which post-processing is performed. Post-processing is also disclosed in U.S. Patent Nos. 5,588,072 and 5,680,479. Post-processing is intended to result in a more compact and "clean" block representation of the document image, and can be tailored to suit particular types of subsequent processing, such as character recognition, data compression, and the like. Generally, however, post-processing involves associating text blocks with other text or non-text blocks. Advantageously, post-processing procedures depend on the page skew determined in step S310. For example, in a case that the page is only slightly skewed, gaps between text blocks are well-defined. Accordingly, blocks are combined aggressively. On the other hand, for a page having a larger skew, circumscribing rectangles likely overlap several text regions, therefore the rectangles are combined more conservatively. Block selection terminates after post-processing. Execution of the Figure 3 process steps results in a detailed representation of the location and type of data within a document image. As a result, the Figure 3 process steps can be used in conjunction with an optical character recognition system so as to recognize characters represented by bit-mapped image data within a table, to convert the characters into an ASCII format, and to input the ASCII characters into a spreadsheet program based on determined row and column table addresses. In addition, the Figure 3 process determines information from which the internal features of a table, such as table grid lines, can be substantially reproduced. Figure 5 is a flow diagram which describes in more detail the steps of the Figure 3 flow diagram, with particular attention paid to analysis of table features. In this regard, steps S501 and S502 correspond to steps S301, S302, S304 and S305 of Figure 3. Particularly, a document image is input in step S501 and connected components within the document image are detected and blocked in step S502. In step S503, as described above with respect to steps S304 and S305, a table, as well as text blocks within the table, is identified. Next, in step S504, invisible lines along the edges of the table are also identified for use in subsequent text block formation. A reversed text area within a subject table is identified in step S505. As mentioned above, a reversed area, which is an area containing text composed of white pixels surrounded by black pixels, is an area to be ignored during subsequent table processing according to the present invention. Figure 6a shows reversed area 42 within table 40. The slanted lines within area 42 represent areas of black pixels, which are arranged so as to form the word "TITLE" within area 42. Also shown in Figure 6a are white areas, represented by dashed lines, which are utilized during identification of table 40, as described with respect to step S409 above. In order to identify reversed areas, flow begins at step S701, in which is calculated a horizontal histogram representing total distances spanned by white boundary pairs within each row of pixels in a table. In this regard, boundary pairs are coordinate locations determined by the intersection of a white area boundary (dashed lines in Figure 6a) with a particular row of pixels. Figure 6b shows histogram 44, which results from step S701 and corresponds to table 40. Flow then proceeds to step S702. Beginning at the top of table 40, each row of pixels is analyzed in step S702, using the dimensions of table 40 and the data reflected in histogram 44, to determine whether the total distance between white boundary pairs within a row of pixels in a table is less than one-half of the total distance spanned by the row. If the foregoing condition is satisfied for N consecutively-analyzed rows, wherein N is a threshold value, flow proceeds to step S704. In the case of table 40, the number of rows in area 42 is greater than N, therefore flow proceeds to step S704. If N consecutive of such rows are not located, flow proceeds to step S506 of Figure 5. In step S704, traced white areas are identified within the N rows identified in step S702. These areas correspond to the interior of each letter formed within area 42. Next, in step S706, the size of each identified white area is compared with a threshold size. The threshold size is preferably defined so as to differentiate text characters or small image noise from larger features. If a size of an identified white area is smaller than the threshold size, it is determined, in step S707, that the white area should be ignored during subsequent table processing. As a result, the white area is not considered during subsequent processing which utilizes white areas within a subject table. Flow then continues to step S709. If, in step S706, a size of an identified white area is not smaller than the threshold size, it is determined that the white area does not correspond to reversed text and flow proceeds to step S709. In step S709, it is determined whether each white area identified in step S704 has been compared with the threshold size. If not, flow returns to step S706 and proceeds as described above. If so, flow proceeds to step S710. Next, in step S710, it is determined whether the last row of table 40 has been analyzed. If not, flow returns to step S702. However, if each row of table 40 has been analyzed, flow proceeds to step S506. In step S506, connected components attached to grid lines within a table are identified and extracted. A system for performing such identification and extraction is described in U.S. Application No. 08/664,674, and therefore will only be described briefly below. In this regard, Figure 8 shows table 50, surrounded on all sides by visible table grid lines. Within table area 50 lies other visible table grid lines, with each white area completely surrounded by black pixels being identified by dashed lines. As shown in Figure 8, several of the visible grid lines include gaps, often caused by poor printing, poor scanning, or the like. These gaps define partial grid lines 51, 52, and 53, also referred to as attached connected components. Due to the proximity of points 56 and 58 of traced white area 54, connected component 52 is extracted, that is, connected component 52 is identified as a connected component separate from the vertical and horizontal grid lines of table area 50. Connected components 51 and 53 are extracted in the same manner. Use of extracted attached connected components is described in detail below. Returning to Figure 5, in step S507, as described with respect to step S308, text blocks and text lines are formed based on connected components located outside each table in the input document image. Similarly, in step S509, text blocks and text lines are formed based on text connected components located within each table. Preferably, in step S507, text blocks are examined to determine whether the text blocks are oriented vertically or horizontally, and text lines are formed based on the determination. On the other hand, only horizontal text lines are preferably formed in step S509. Each text block formed within a table is referred to hereinbelow as a table cell. In this regard, as described above, text connected components which are not combined with other text connected components within a table block are also referred to herein as table cells. Next, in step S510, table columns are calculated and column addresses are assigned to table cells. Figures 9a and 9b are detailed flow diagrams for describing the process of step S510. The Figure 9a and 9b flow utilizes a vertical histogram of a table in order to identify table columns and, advantageously, ignores title areas, which may disrupt proper column identification. In this regard, in step S901 it is determined whether a subject table contains more than four text lines. If so, the top-most text line is assumed to be a title line and is ignored during column identification. For example, as shown in Figure 10, table 60 is determined in step S901 to contain more than four rows of text lines. Accordingly, flow proceeds to step S902. In step S902, a vertical histogram, such as vertical histogram 70 of Figure 10b, is calculated. As mentioned above, vertical histogram 70 is calculated ignoring top-most text line 61, which is assumed to be a title. If the determination in step S901 is negative, flow proceeds directly to step S904, in which a vertical histogram is calculated without ignoring any text lines in the subject table. It should be noted that the vertical histograms described in Figure 9a reflect, for each column of pixels in table 60, total vertical distances spanned by rectangles (not shown) circumscribing each connected component within table 60 and described with respect to steps S302 and S502. For example, horizontal and vertical grid lines such as grid lines 64 and 66 are not blocked by circumscribing rectangles and therefore do not contribute to the data reflected in vertical histogram 70. However, it should also be noted that connected components identified and extracted in step S506 are "blocked" and are therefore reflected in histograms calculated according to step S904 or step S902. For example, attached connected component 68 is reflected by portion 71 of histogram 70. In step S905, a distance over which a calculated histogram reflects a small amount of black pixels, P_gap, a portion of a histogram reflecting relatively more pixels and immediately following P_gap, W, and a histogram portion reflecting a small amount of pixels which immediately follows W, N_gap, are calculated beginning from the left-most point of histogram 70. For example, P_gap, W, and N_gap initially correspond to areas 72, 74, and 75, respectively, of histogram 70. Columns are then assigned according to the respective locations and sizes of P_gap, W, and N_gap. According to steps S906 to S916 (shown in Figure 9b), a table is analyzed from left to right. In this regard, steps S906 to S916 determine whether a table region corresponding to a current W should be included in a current column, whether a new column should be defined including the current W, or whether a new column should be defined ignoring the current W. Initially, it is determined in step S906 whether W is greater than a threshold value, which preferably reflects a small text character size. If so, flow proceeds to step S907, in which it is determined whether P_gap is narrow. If P_gap is not narrow, flow proceeds to step S917, in which a new column is defined including W. This configuration of W and P_gap is illustrated in Figure 11a. If it is determined in step S907 that P_gap is narrow, flow proceeds to step S909, wherein the definition of the current column is extended to include W. This situation is shown in Figure 11b. Flow then returns to step S905. If, in step S906, it is determined that W is not greater than the threshold value, flow proceeds to step S910, in which it is determined whether P_gap is wide. If so, flow proceeds to step S911, where it is determined whether W reflects a cut connected component, such as connected component 52 of table 50. If W does not reflect a cut connected component, flow proceeds to step S917 wherein a new column is defined, which includes W. Figure 11c reflects this situation. If, in step S911, it is determined that W reflects a cut connected component, as shown in Figure 11d, the right-most border of the current column is defined at the left edge of P_gap, and flow returns to step S905 to define a new P-gap, W, and N_gap. Accordingly, the reflected cut connected component is not included in any defined column. Returning to step S910, if it is determined that P_gap is not wide, flow proceeds to step S912. In step S912, it is determined whether N_gap is wide. If not, flow proceeds to step S914 wherein it is determined whether the current column is the first column in the subject table. If so, flow proceeds to step S917 as described above. Such a situation is reflected in Figure 11e, wherein the current column is defined to terminate at the initial point of N_gap. If, in step S914, it is determined that the current column is not the first column in the subject table, flow proceeds to step S915, wherein the current column is extended to include W. Such a situation is shown in Figure 11f. Flow then returns to step S905. If N_gap is determined to be wide in step S912, flow proceeds to step S916, where it is determined whether W reflects a cut connected component. If not, flow proceeds to step S915 in order to include W, shown in Figure 11g, into the current column. If the determination in step S916 is affirmative, the right-most border of the current column is defined at the left edge of P-gap, and flow simply returns to step S905 so as to ignore the cut connected component. This situation is shown in Figure 11h. Returning to Figure 9a, flow proceeds from step S917 to step S918. If the right-most portion of a histogram reflecting the subject table has been reached at this point, flow continues to step S920. If not, flow returns to step S905. Upon reaching step S920, several columns are defined within a subject table. These columns are addressed sequentially from left to right and are defined according to left and right X-coordinate boundaries. In this regard, Figures 12a and 12b show table 76 and corresponding column definitions A and B. As shown in Figure 12b and due to table cell 79, column A should be split into two separate columns. Steps S920 and S921 are meant to address this situation. In step S920, left and right boundaries of detected white areas and cut vertical grid lines are examined to determine whether these features lie close to any defined column boundaries. If so, flow continues to step S922. If any detected white boundaries or cut grid lines are not close to a column boundary, flow proceeds to step S921, in which a new column is defined surrounding the white boundaries or cut grid lines. All column addresses are then updated accordingly. Figure 12c shows the column definitions of table 76 after step S921. In particular, because the right boundary of white area 78 is not close to the boundary of any defined column, a new column B is defined adjacent to the right boundary of area 78. Accordingly, the address of the previous column B is updated to column C. Next, in step S922, each table cell is assigned a column address corresponding to the column locations spanned by the table cell. In some cases, the assigned column address may include a range of column addresses. For example, table cell 77 is assigned column address A, while table cell 79 is assigned column address A to B. Flow proceeds from Step S922 to step S511 in order to calculate table rows and assign row addresses to table cells. Figure 13 is a detailed flow diagram describing the process steps executed in step S511 to calculate table rows and assign row addresses to table cells. Flow begins in step S1301, in which a horizontal histogram of a subject table is created. Figure 14 illustrates subject table 80 and horizontal histogram 86 created in accordance with step S1301. Horizontal histogram 86 reflects, for each row of pixels in table 80, horizontal distances spanned by rectangles (not shown) circumscribing each connected component within table 80. In this regard, peak width pw1 of histogram 86 reflects table cells 81 and 82, peak width pw2 reflects table cell 84, and peak width pw3 reflects table cell 83. Valley widths vw1 and vw2 indicate regions between peak width pw1 and peak width pw2, and between peak width pw2 and peak width pw3, respectively. In step S1302, the upper-most peak in histogram 86 is initially examined. Particularly, it is determined whether an initial peak width pw in a current row is greater than a threshold value, the threshold value preferably being indicative of a minimum character size, and whether a next valley width vw is greater than a second threshold value, the second threshold value preferably being indicative of a minimum row spacing. If both conditions are satisfied, flow proceeds to step S1304, wherein a new row is defined corresponding to a next-encountered peak. If one of the conditions is not satisfied in step S1302, flow proceeds to step S1305, in which the current row is extended so as to include a next-encountered peak. In either case, flow proceeds from step S1304 and step S1305 to step S1306. In step S1306 it is determined whether additional horizontal histogram peaks exist. If so, flow returns to step S1302. If not, flow proceeds to step S1307. In step S1307, horizontal white area boundaries and attached horizontal grid lines within table 80 are identified. Similarly to above-described steps S920 to S922, in a case that an identified white boundary or grid line is not close to any row boundaries, a new row is defined and row addresses are updated accordingly. Flow then proceeds to step S512. In step S512, vertical and horizontal grid line locations are calculated. Figure 15 is a flow diagram describing the process steps of step S512 in detail. Flow begins at step S1501, wherein it is determined whether a current and an adjacent column are each surrounded by corresponding traced white areas. If so, flow proceeds to step S1502. In step S1502, a distance between the corresponding white areas is determined. Next, in step S1504, if the distance determined in step S1502 is not smaller than a threshold distance, flow proceeds to step S1505, wherein a vertical grid line coordinate is calculated. If it is determined in step S1504 that the distance is smaller than a threshold value, flow proceeds to step S1506, wherein a vertical grid line coordinate is calculated based on, preferably, a different equation than that used in step S1505. If it is determined in step S1501 that white areas corresponding to adjacent columns do not exist, flow proceeds to step S1510 in which a vertical grid line coordinate is calculated using an equation different than those used in step S1505 and step S1506. Flow then proceeds to step S1511, at which it is determined whether more columns exist. If so, flow returns to step S1501. If not, flow proceeds to step S1512. In step S1512, horizontal grid lines for a subject table are calculated using steps substantially similar to those described in steps S1501 to S1511, although rotated through 90 degrees. Figure 16 illustrates two subject tables for describing the Figure 15 flow. With respect to Figure 16a, table 90 contains column C1 and column C2, column C1 being bounded by X-coordinates C1L and C1R and with column C2 being bounded by X-coordinates C2L and C2R. Traced white area 91 corresponds to column C1 and contains border b1, and traced white area 92 corresponds to column C2 and contains border b2. Accordingly, in step S1501, it is determined that white areas corresponding to each of adjacent columns C1 and C2 exist. At step S1502, the distance between b1 and b2 is determined. In a case that areas 91 and 92 contain regions sharing a common X-coordinate, for example, in a case that a slanted vertical line separates areas 91 and 92, border b1 is defined as the right-most X-coordinate of area 91, and b2 is defined as the left-most X-coordinate of area 92. If the distance between b1 and b2 is not judged to be small in step S1504, flow proceeds to step S1505. In a preferred embodiment, the coordinate location of a vertical grid line between columns C1 and C2 is determined in step S1505 by the equation min(max((T,C1R), C2L), wherein T = max(b1,b2) - 1. If the distance between b1 and b2 is judged to be small in step S1504, flow proceeds to step S1506, wherein a vertical grid line coordinate is calculated to exist at min[max((b1 + b2)/2, C1R, C2L)]. Figure 16b shows table 95 in which column C3 is bounded by coordinates C3L and C3R and wherein column C4 is bounded by coordinates C4L and C4R. It should be understood that because column C3 is not bounded on both sides by a visible grid line, no white area exists corresponding to column C3. Accordingly, in a case that column C3 and column C4 are examined as adjacent columns using the process steps described in Figure 15, the determination in step S1501 is negative and flow proceeds to step S1510. In step S1510, a vertical grid line location corresponding to columns C3 and C4 is determined by the equation min(T,C4L) wherein T = max(C3R, b4) - 1. After executing step S1512 with respect to horizontal grid lines, flow proceeds to step S514, in which super-cells are identified. Figure 17 is a detailed flow diagram for describing super-cell identification in accordance with step S514. In this regard, at step S1701, super-cells are identified corresponding to each white area bordered by black pixels within a subject table. In a case that a table cell exists which is not surrounded by a traced white area, an "artificial" super-cell is also formed in step S1701 having the same location and dimensions of the table cell. Next, in step S1702, each artificial super-cell is reformatted to approximate dimensions of surrounding visible or non-visible table grid lines. Flow then continues to step S1704. In step S1704, the top, bottom, left, and right borders of each super-cell are examined to identify whether or not visible grid lines are adjacent to the borders. Flow proceeds to step S1705, wherein row and column addresses are assigned to the super-cells based on the rows and columns calculated in steps S510 and S511. Flow continues from step S1705 to step S515. Figure 18 shows a subject table for describing the Figure 17 flow diagram. In this regard, table 100 contains table cells 101, 102, 104 and 105. Table cell 101 is not surrounded by a white area because table cell 101 does not lie in an area completely surrounded by black pixels. However, table cells 102, 104 and 105 are surrounded by white areas 106, 107 and 109. With respect to the Figure 17 flow, super-cells are initially defined in step S1701 corresponding to each white area within table 100. Accordingly, super-cells are defined corresponding to white areas 106, 107 and 109. These super-cells are shown in Figure 18b as super-cells 115, 111 and 112. As described above, because no white area surrounds table cell 101, an artificial super-cell is also defined having identical coordinates to those of table cell 101. In step S1702, the artificial super-cell is reformatted according to the following algorithm to approximate visible or non-visible table grid lines surrounding the artificial super-cell: Top border = min(grid_line_top, text_cell_top); Bottom border = max(grid_line_bottom, text_cell_bottom); Left border = min(grid_line_left, text_cell_left); and Right border = max(grid_line_right, text_cell_right). Resulting super_cell 114 is shown in Figure 18b. Next, in step S1704, each super-cell is examined to identify visible grid lines bordering the super-cell. For example, visible grid lines are identified at the right and bottom of super-cell 114, at the top, right, left and bottom of super-cell 115, at the top, right, left and bottom of super-cell 112, and at the top, right, left and bottom of super-cell 111. It should be noted that, in a case that a particular super-cell is partially bordered on a particular side by a partially visible grid line, it is determined in step S1704 whether the partial grid line exceeds a certain threshold length corresponding to the length of a "complete" border. If so, it will be determined in step S1704 that the partial grid line completely borders the particular side of the particular super-cell. Flow proceeds from step S1704 to step S1705, wherein row and column addresses are assigned to each super-cell of Figure 18b. In Figure 18b, the super-cells share the same column and row locations as their respective table cells. Accordingly, super-cells 111, 112, 114 and 115 also share the row and column addresses of table cells 104, 105, 101 and 102, respectively. It should be noted that, in a case that a super-cell is located within several rows or columns, the super-cell address will contain a row and/or column address range, such as (1, 2-4). Flow then proceeds to step S515. In step S515, table cells having a row and/or column address range are split, if appropriate. In this regard, the foregoing process steps may result in table cells spanning several rows or columns which should be more properly classified as multiple table cells. For example, Figure 19, comprising Figures 19a, 19b, and 19c, illustrates several table cells having a column address range. Similarly, Figure 20 illustrates a table cell having a row address range. In this regard, Figure 21a and Figure 21b describe steps for splitting table cells having a column address range and a row address range, respectively. In order to split table cells having a column address range, flow begins at step S2101. In step S2101, a table cell having a column address range is identified. Next, in step S2102, a vertical histogram of the identified table cell is calculated. The vertical histogram reflects, for each column of pixels in the identified table cell, the total vertical distance spanned by rectangles (not shown) circumscribing each connected component in the table cell. Flow proceeds to step S2104, wherein it is determined whether an area of the histogram corresponding to a space between adjacent columns of the table cell reflects any black pixels. If any black pixels are reflected, flow continues to step S2105. In step S2105, it is determined whether the histogram of the area between the columns is non-zero for a continuous distance greater than a threshold distance. If so, flow proceeds to step S2114, in which it is determined whether additional columns exist within the address range of the subject table cell. Such a situation is illustrated in Figure 19a, in which 1 indicates a continuous non-zero portion of a histogram between columns r and r+1. If additional columns exist, flow returns to step S2104. In a case that additional columns do not exist in step S2114, it is determined in step S2115 whether the subject table contains additional table cells having a column address range. If so, flow returns to step S2102. If not, flow continues to step S2116 of Figure 21b. Figure 19b illustrates a situation in which, in step S2105 of Figure 21a, it is determined that continuous non-zero portion m is less than a threshold. Accordingly, flow proceeds to step S2106, in which it is determined whether a separate non-zero portion is located between columns s and s+1 of Figure 19b. If not, table cell 116 is redefined as two new table cells, one having column address s and the other having column address s+1, such that neither of the two new cells contain portion m. If a separate non-zero portion is located between columns s and s+1 in step S2106, flow continues to step S2114 and continues as described above. In a case that, in step S2104, it is determined that the area between two columns of a subject table cell does not contain black pixels, flow proceeds to step S2110. In step S2110, the area between the two columns is extended into the two columns until a black pixel is encountered in each column. As shown in Figure 19c, area n between columns t and t+1 of table cell 117 contains no black pixels. Accordingly, in step S2110, area n is extended in both horizontal directions until black pixels within table cell 117 are encountered. The resulting area is indicated as n'. In step S2111, it is determined whether the length of an area such as area n' is greater than a threshold value. If so, table cell 117 is split into two table cells in step S2112 and flow continues to step S2114. If not, flow simply proceeds to step S2114 and eventually to step S2116 of Figure 21b. The process steps of Figure 21b are used to split table cells having a row address range according to the present invention. In step S2116, a subject table cell having a row address range is retrieved. Next, in step S2117, two rows in the row address range are examined to determine a distance between the rows and a mid-point between the rows. These two quantities are represented by row_dist and mid_row in Figure 20. In Figure 20, a table cell includes row R1 and row R2 in its row address range. The upper bound of row R1 is defined by coordinate R1U and the lower bound of row R1 is define by coordinate R1L. Similarly, the upper bound of row R2 is defined by R2U and the lower bound is defined by R2L. As shown in Figure 20, the quantity mid-_row refers to the mid-point between R1L and R2U. In addition, the quantity row_dist refers to the distance between R1L and R2U. Next, in step S2119, it is determined whether the retrieved table cell includes consecutive text lines located on opposite sides of mid_row and whether row_dist is greater than a threshold line size. If so, flow proceeds to step S2120, wherein the subject table cell is cut into two cells at the mid_row coordinate, one of the two cells containing row R1 in its row address and the other cell containing row R2 in its row address. Flow then proceeds to step S2121. If the determination in step S2119 is negative, flow also proceeds to step S2121, wherein it is determined whether the subject table cell contains additional rows. If so, flow returns to step S2117. If not, flow continues to step S2122, in which it is determined whether other table cells having a row address range exist in the subject table. If so, flow returns to step S2116. If not, flow continues to step S516 in order to delete spurious rows and columns resulting from the flow of steps S510 to S515. Figure 22 is a diagram for describing the process steps of step S516. For example, as shown in Figure 22a, numeral 130 indicates a right border of column C5. Right border 130 is located adjacent to vertical grid line 132. Since no connected components exist in column C6, located between right column border 130 and vertical grid line 132, column C6 is deleted by eliminating border 130. Accordingly, grid line 132 will be newly defined as the right-most border of column C5. Figure 22b illustrates a second situation for which column locations are updated in step S516. As shown in Figure 22b, slanted line 134 has caused column C9 to be defined between left border 135 and right border 136. Accordingly, column C9 is deleted by replacing borders 135 and 136 by a single border (not shown) between newly-expanded columns C8 and C10. Flow continues from step S516 to step S517. In step 517, row and column addresses of super-cells are adjusted so as to reduce incorrect address assignment due to slanted table grid lines. For example, Figure 23 illustrates a situation in which a slanted table grid line has caused incorrect row and column address assignment. In particular, slanted vertical grid line 140 intersects columns u and u+1, which are defined at their right and left borders, respectively, by non-visible grid line 141. Due to slanted line 140, super-cells 143 to 145 have a column address range of (u - u+1), even though the black pixels contained within cells 143 to 145 exist completely within column u+1. Several criteria are evaluated in step S517 to determine whether a column address of a super-cell should be updated to exclude a left-most column. First, it is determined whether a left border of a subject super-cell lying in column u+1 is located in column u. Second, it is determined whether a distance between the left border and grid line 141 is less than a threshold value. Third, it is determined whether all connected components within the super-cell are to the right of grid line 141. Fourth, it is determined whether a super-cell exists within column u at the row addresses of the subject super-cell. If these four criteria are satisfied, column u is deleted from the definition of the subject super-cell and the subject super-cell dimensions are redefined. In the case of Figure 23, super-cells 143 to 145 satisfy the above criteria. A resulting redefinition of super-cells 143 to 145 is shown in Figure 24. Figure 24 also reflects redefinition of super-cells 148 and 149 based on a modified version of the above criteria, which is used to determine whether a right-most column should be deleted from a super-cell column address range. According to the modified criteria, it is first determined whether a right border of a subject super-cell lying in column u is located in column u+1. Second, it is determined whether a distance between the right border and grid line 141 is less than a threshold value. Third, it is determined whether all connected components within the super-cell are to the left of grid line 141. Fourth, it is determined whether a super-cell exists within column u+1 at the row addresses of the subject super-cell. If these four criteria are satisfied, column u+1 is deleted from the definition of the subject super-cell and the subject super-cell dimensions are redefined. It should be understood that the foregoing criteria can be adapted similarly to address forward-slanting grid lines, rather than backward-slanting grid line 140 of Figures 23 and 24. In this regard, criteria similar to the above are also used in step S517 to adjust super-cell row addresses due to slanted horizontal lines. Step S519 is executed after step S517 so as to create dummy super-cells or to extend a super-cells in case a "super-cell hole" exists. Figure 26 is an example showing situations in which "super-cell holes" arise. As shown in Figure 26a, no visible grid line exists to the left of table cell 160. Therefore, as described above with respect to the method of Figure 17, super-cell is created, and is bordered by non-visible grid lines to its left and bottom. White area 163 is surrounded by black pixels, therefore super-cell 164 was assigned in step S1701 corresponding to white area 163. In contrast, since area 165 neither contains a connected component nor a white area surrounded completely by black pixels, a super-cell has not been assigned to area 165. As described, "super-cell holes" exist between the lower border of super-cell 162 and horizontal grid line 161, and in area 165. The flow diagram illustrated in Figure 25 addresses the situation shown in Figure 26a. In particular, flow begins at step S2501, in which each super-cell border is extended to a corresponding visible grid line. Super-cell size and border information is then updated in step S2504. In step S2505, the left, right top and bottom edges of a subject table are analyzed to determine whether any areas such as area 165 exist. If so, a dummy super-cell is created surrounding each of such areas in step S2506. Flow then proceeds to step S520. If no areas such as area 165 exist, flow proceeds directly from step S2505 to step S520. After application of the Figure 25 steps, super-cell 162 is extended as shown in Figure 26b. Also shown in Figure 26b is dummy super-cell 167 corresponding to area 165. Returning to the processing described in Figure 5, step S520 includes steps to create a dummy cell in a case that a "cell hole" exists. Figure 27 shows a table containing a cell hole. In particular, table 170 contains, among other things, super-cell 172. Within super-cell 172 are table cells 174, 175 and 176. Based on the row and column addresses of table cells 174 to 176 and on the dimensions of super-cell 172, it is determined that a table cell is missing from location 177, indicated by a dash line. This location is referred to as a cell hole. Accordingly, in step S520, a cell is defined at location 177. Of course, the cell defined in step S520 does not contain any connected components. However, such a cell is useful for defining and quickly reproducing table features. Moreover, such a cell allows data to be easily added at location 177 to a file representing table 170 because the file will contain a pre-defined cell at location 177. Figure 28 is an example of a table having visible table cell borders according to step S521. Particularly, Figure 28a shows table 180 containing table cells 181 to 186. Also shown are super-cell 187, which contains table cells 181 and 182, and super-cell 188, which contains table cells 183 to 186. In step S521, each table cell is examined to determine whether it is bounded by a visible grid line on its top, bottom, left, or right. In this regard, table cell 181 is bounded on its top and right sides, table cell 182 is bounded on its right and bottom sides, table cell 183 is bounded on its top and left sides, table cell 184 is bounded on its left and bottom sides, table cell 185 is bounded on its bottom and right sides, and table cell 186 is bounded on its top and right sides. The determined information can be stored along with definitions of table cell type, table cell row and column address, and table cell location and size. Figure 28b is an example for describing storing data regarding visible borders surrounding table cells. In particular, Figure 28b shows a matrix representing each table cell of table 180. As shown in the matrix, and corresponding to Figure 28a, the circles in Figure 28b indicate non-visible table cell borders, while the dashes indicate visible table cell borders. By reference to a matrix such as that shown in Figure 28b, internal grid lines of a table analyzed according to the present invention can be quickly reproduced. Flow continues from step S521 to S522, wherein skew detection and post-processing is performed as described above with respect to steps S310 and S311 of Figure 3. It should be noted that the information determined by the Figure 5 flow is preferably stored in a hierarchical tree structure representing a subject table. In a preferred embodiment, data regarding super-cells, which includes row and column addresses of a super-cell, super-cell coordinates, table cells included within a super-cell, and information regarding the existence of visible borders of the super-cell, are stored in a node representing the table block, such as node 200 in Figure 29a. Node 200 also stores table coordinates, column and row range, and location of visible and invisible vertical and horizontal grid lines. Descending from node 200 are table cell nodes 202 to 204. These table cell nodes contain data representative of table cell coordinates, table cell row and column addresses, the existence of visible table cell borders, and surrounding super-cells are stored in nodes representative of a subject table cell. Figure 29b shows an alternative representation of the Figure 29a hierarchical tree. In the tree of Figure 29b, super-cell nodes 205 and 206 are defined separately from table node 200, and have descending therefrom nodes representative of table cells contained within a corresponding super-cell. It should be understood that in the Figure 29b tree structure, super-cell nodes 205 and 206, rather than node 200, contain data regarding the super-cell frames, which includes column and row addresses of a super-cell, super-cell coordinates, table cells included within a super-cell, and information regarding the existence of visible borders of the super-cell. While the present invention is described above with respect to what is currently consider its preferred embodiment, it is to be understood that the invention is not limited to that described above. To the contrary, the invention is intended to cover various modifications and equivalent arrangements includes within the scope of the appended claims.

Claims: A method for analyzing a table image, the method comprising the steps of: identifying table cells in a row and column structure, the table cell dimensions corresponding to detected positions of text blocks of the table image (S503-S512); determining whether vertical and horizontal grid lines bounding each table cell are visible or non-visible (S504); identifying super-cells that include one or more table cells, wherein when the bounding grid lines are visible, super-cells are identified according to traced white areas surrounding table cells and bounded by visible grid lines, and wherein when not all bounding grid lines are visible, a super-cell is defined around a table cell so that it has the approximate dimensions of surrounding visible or non-visible table grid lines (S1701, S1702)); determining whether vertical and horizontal grid lines bounding each super-cell are visible or non-visible (S1704); and assigning row and column addresses to the super-cells (S1705). A method according to claim 1, further comprising the steps of: calculating a vertical histogram reflecting connected components within the table image (S904); defining columns within the table image according to the vertical histogram (S917); and redefining the columns based on locations of traced white areas and grid lines with respect to the defined columns (S921). A method according to claim 2, further comprising the step of assigning column addresses to each table cell based on the redefined columns (S922). A method according to claim 2, further comprising the step of detecting areas of reversed text within the table image (S505), wherein said calculating step calculates the vertical histogram reflecting connected components within the table image, the histogram not reflecting connected components within the detected areas of reversed text. A method according to claim 4, wherein the step of detecting areas of reversed text comprises: calculating a horizontal histogram of the table image reflecting white pixels within the image (S701); identifying consecutive rows within the table image in which a total distance between boundary pixels in a row is less than one half of a total length of the row (S702); detecting a traced white area within the consecutive rows (S704); and determining whether the detected traced white area corresponds to reversed text according to the size of the traced white area. A method according to claim 1, further comprising the steps of: calculating a horizontal histogram reflecting connected components within the table image (S502); defining rows within the table image according to the horizontal histogram; and redefining the rows based on locations of traced white areas and grid lines with respect to the defined rows. A method according to claim 6, further comprising the step of assigning row addresses to each table cell based on the redefined rows (S1307). A method according to claim 6, further comprising the step of detecting areas of reversed text within the table image (S505), wherein said calculating step calculates the horizontal histogram reflecting connected components within the table image, the histogram not reflecting connected components within the detected areas of reversed text (S701). A method according to claim 2, further comprising the step of assigning a column address to each super-cell based on.the redefined columns (S1705). A method according to claim 6, further comprising the step of assigning a row address to each super-cell based on the redefined rows (S1705). A method according to claim 1, wherein said step of identifying super-cells identifies an artificial super-cell as the super-cell (S1702), wherein at least one of the grid lines of the artificial super-cell include the non-visible grid lines along the edges of the table image. A method according to claim 1, wherein said step of identifying super-cells identifies a dummy super-cell (167) as the super-cell (S519), wherein the dummy super-cell is identified at a super-cell hole which does not include the table cell, wherein at least one of the grid lines of the dummy super-cell include the non-visible grid lines along the edges of the table image (S521). A method according to any one of the preceding claims, further comprising the step of storing data regarding the identified super-cells and the identified table cells in a hierarchical tree structure (200-206) which reflects a physical layout of the table image. A method according to claim 1, wherein said step of identifying table cells identifies a dummy table cell as the table cell, the dummy table cell being identified as a cell hole which does not include text blocks (S521). A method according to claim 2, further comprising a step of redefining the columns in a case that a slanted visible grid line exits within the table image (S517). A method according to claim 6, further comprising the step of rendering the rows in a case that a slanted visible grid line exits within the table image (S517). A method according to claim 8, further comprising the step of detecting areas of reversed text within the table image, wherein said calculating step calculates the vertical histogram reflecting connected components within the table image, the histogram not reflecting connected components within the detected areas of reversed text (S904). A computer program comprising processor implementable instructions for performing all the steps of a method according to any one of claims 1 to 17. A computer software medium storing a computer program according to claim 18. A signal carrying processor implementable instructions for controlling a computer to carry out all the steps of a method according to any one of claims 1 to 17.

Inventors: WANG SHIN-YWAN, WANG, SHIN-YWAN
Applicants: CANON KK, CANON KABUSHIKI KAISHA
Application year: 1998
Main classification: G06K9
IPC codes: G06K9, G06T7