Image::Leptonica::Func::classapp
version 0.04
classapp.c
classapp.c Top-level jb2 correlation and rank-hausdorff l_int32 jbCorrelation() l_int32 jbRankHaus() Extract and classify words in textline order JBCLASSER *jbWordsInTextlines() l_int32 pixGetWordsInTextlines() l_int32 pixGetWordBoxesInTextlines() Use word bounding boxes to compare page images NUMAA *boxaExtractSortedPattern() l_int32 numaaCompareImagesByBoxes() static l_int32 testLineAlignmentX() static l_int32 countAlignedMatches() static void printRowIndices()
NUMAA * boxaExtractSortedPattern ( BOXA *boxa, NUMA *na )
boxaExtractSortedPattern() Input: boxa (typ. of word bounding boxes, in textline order) numa (index of textline for each box in boxa) Return: naa (numaa, where each numa represents one textline), or null on error Notes: (1) The input is expected to come from pixGetWordBoxesInTextlines(). (2) Each numa in the output consists of an average y coordinate of the first box in the textline, followed by pairs of x coordinates representing the left and right edges of each of the boxes in the textline.
l_int32 jbCorrelation ( const char *dirin, l_float32 thresh, l_float32 weight, l_int32 components, const char *rootname, l_int32 firstpage, l_int32 npages, l_int32 renderflag )
jbCorrelation() Input: dirin (directory of input images) thresh (typically ~0.8) weight (typically ~0.6) components (JB_CONN_COMPS, JB_CHARACTERS, JB_WORDS) rootname (for output files) firstpage (0-based) npages (use 0 for all pages in dirin) renderflag (1 to render from templates; 0 to skip) Return: 0 if OK, 1 on error Notes: (1) The images must be 1 bpp. If they are not, you can convert them using convertFilesTo1bpp(). (2) See prog/jbcorrelation for generating more output (e.g., for debugging)
l_int32 jbRankHaus ( const char *dirin, l_int32 size, l_float32 rank, l_int32 components, const char *rootname, l_int32 firstpage, l_int32 npages, l_int32 renderflag )
jbRankHaus() Input: dirin (directory of input images) size (of Sel used for dilation; typ. 2) rank (rank value of match; typ. 0.97) components (JB_CONN_COMPS, JB_CHARACTERS, JB_WORDS) rootname (for output files) firstpage (0-based) npages (use 0 for all pages in dirin) renderflag (1 to render from templates; 0 to skip) Return: 0 if OK, 1 on error Notes: (1) See prog/jbrankhaus for generating more output (e.g., for debugging)
JBCLASSER * jbWordsInTextlines ( const char *dirin, l_int32 reduction, l_int32 maxwidth, l_int32 maxheight, l_float32 thresh, l_float32 weight, NUMA **pnatl, l_int32 firstpage, l_int32 npages )
jbWordsInTextlines() Input: dirin (directory of input pages) reduction (1 for full res; 2 for half-res) maxwidth (of word mask components, to be kept) maxheight (of word mask components, to be kept) thresh (on correlation; 0.80 is reasonable) weight (for handling thick text; 0.6 is reasonable) natl (<return> numa with textline index for each component) firstpage (0-based) npages (use 0 for all pages in dirin) Return: classer (for the set of pages) Notes: (1) This is a high-level function. See prog/jbwords for example of usage. (2) Typically, words can be found reasonably well at a resolution of about 150 ppi. For highest accuracy, you should use 300 ppi. Assuming that the input images are 300 ppi, use reduction = 1 for finding words at full res, and reduction = 2 for finding them at 150 ppi.
l_int32 numaaCompareImagesByBoxes ( NUMAA *naa1, NUMAA *naa2, l_int32 nperline, l_int32 nreq, l_int32 maxshiftx, l_int32 maxshifty, l_int32 delx, l_int32 dely, l_int32 *psame, l_int32 debugflag )
numaaCompareImagesByBoxes() Input: naa1 (for image 1, formatted by boxaExtractSortedPattern()) naa2 (ditto; for image 2) nperline (number of box regions to be used in each textline) nreq (number of complete row matches required) maxshiftx (max allowed x shift between two patterns, in pixels) maxshifty (max allowed y shift between two patterns, in pixels) delx (max allowed difference in x data, after alignment) dely (max allowed difference in y data, after alignment) &same (<return> 1 if @nreq row matches are found; 0 otherwise) debugflag (1 for debug output) Return: 0 if OK, 1 on error Notes: (1) Each input numaa describes a set of sorted bounding boxes (sorted by textline and, within each textline, from left to right) in the images from which they are derived. See boxaExtractSortedPattern() for a description of the data format in each of the input numaa. (2) This function does an alignment between the input descriptions of bounding boxes for two images. The input parameter @nperline specifies the number of boxes to consider in each line when testing for a match, and @nreq is the required number of lines that must be well-aligned to get a match. (3) Testing by alignment has 3 steps: (a) Generating the location of word bounding boxes from the images (prior to calling this function). (b) Listing all possible pairs of aligned rows, based on tolerances in horizontal and vertical positions of the boxes. Specifically, all pairs of rows are enumerated whose first @nperline boxes can be brought into close alignment, based on the delx parameter for boxes in the line and within the overall the @maxshiftx and @maxshifty constraints. (c) Each pair, starting with the first, is used to search for a set of @nreq - 1 other pairs that can all be aligned with a difference in global translation of not more than (@delx, @dely).
l_int32 pixGetWordBoxesInTextlines ( PIX *pixs, l_int32 reduction, l_int32 minwidth, l_int32 minheight, l_int32 maxwidth, l_int32 maxheight, BOXA **pboxad, NUMA **pnai )
pixGetWordBoxesInTextlines() Input: pixs (1 bpp, typ. 300 ppi) reduction (1 for input res; 2 for 2x reduction of input res) minwidth, minheight (of saved components; smaller are discarded) maxwidth, maxheight (of saved components; larger are discarded) &boxad (<return> word boxes sorted in textline line order) &naindex (<optional return> index of textline for each word) Return: 0 if OK, 1 on error Notes: (1) The input should be at a resolution of about 300 ppi. The word masks can be computed at either 150 ppi or 300 ppi. For the former, set reduction = 2. (2) This is a special version of pixGetWordsInTextlines(), that just finds the word boxes in line order, with a numa giving the textline index for each word. See pixGetWordsInTextlines() for more details.
l_int32 pixGetWordsInTextlines ( PIX *pixs, l_int32 reduction, l_int32 minwidth, l_int32 minheight, l_int32 maxwidth, l_int32 maxheight, BOXA **pboxad, PIXA **ppixad, NUMA **pnai )
pixGetWordsInTextlines() Input: pixs (1 bpp, typ. 300 ppi) reduction (1 for input res; 2 for 2x reduction of input res) minwidth, minheight (of saved components; smaller are discarded) maxwidth, maxheight (of saved components; larger are discarded) &boxad (<return> word boxes sorted in textline line order) &pixad (<return> word images sorted in textline line order) &naindex (<return> index of textline for each word) Return: 0 if OK, 1 on error Notes: (1) The input should be at a resolution of about 300 ppi. The word masks and word images can be computed at either 150 ppi or 300 ppi. For the former, set reduction = 2. (2) The four size constraints on saved components are all scaled by @reduction. (3) The result are word images (and their b.b.), extracted in textline order, at either full res or 2x reduction, and with a numa giving the textline index for each word. (4) The pixa and boxa interfaces should make this type of application simple to put together. The steps are: - optionally reduce by 2x - generate first estimate of word masks - get b.b. of these, and remove the small and big ones - extract pixa of the word images, using the b.b. - sort actual word images in textline order (2d) - flatten them to a pixa (1d), saving the textline index for each pix (5) In an actual application, it may be desirable to pre-filter the input image to remove large components, to extract single columns of text, and to deskew them. For example, to remove both large components and small noisy components that can interfere with the statistics used to estimate parameters for segmenting by words, but still retain text lines, the following image preprocessing can be done: Pix *pixt = pixMorphSequence(pixs, "c40.1", 0); Pix *pixf = pixSelectBySize(pixt, 0, 60, 8, L_SELECT_HEIGHT, L_SELECT_IF_LT, NULL); pixAnd(pixf, pixf, pixs); // the filtered image The closing turns text lines into long blobs, but does not significantly increase their height. But if there are many small connected components in a dense texture, this is likely to generate tall components that will be eliminated in pixf.
Zakariyya Mughal <zmughal@cpan.org>
This software is copyright (c) 2014 by Zakariyya Mughal.
This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.
To install Image::Leptonica, copy and paste the appropriate command in to your terminal.
cpanm
cpanm Image::Leptonica
CPAN shell
perl -MCPAN -e shell install Image::Leptonica
For more information on module installation, please visit the detailed CPAN module installation guide.