Document Layout Analysis

QURATOR-SPK

Last update: Dec 29, 2022

Related tags

Overview

Eynollah

Document Layout Analysis

Introduction

This tool performs document layout analysis (segmentation) from image data and returns the results as PAGE-XML.

It can currently detect the following layout classes/elements:

In addition, the tool can be used to detect the ReadingOrder of regions. The final goal is to feed the output to an OCR model.

The tool uses a combination of various models and heuristics (see flowchart below for the different stages and how they interact):

The first three stages are based on pixel-wise segmentation.

Border detection

For the purpose of text recognition (OCR) and in order to avoid noise being introduced from texts outside the printspace, one first needs to detect the border of the printed frame. This is done by a binary pixel-wise-segmentation model trained on a dataset of 2,000 documents where about 1,200 of them come from the dhSegment project (you can download the dataset from here) and the remainder having been annotated in SBB. For border detection, the model needs to be fed with the whole image at once rather than separated in patches.

Layout detection

As a next step, text regions need to be identified by means of layout detection. Again a pixel-wise segmentation model was trained on 131 labeled images from the SBB digital collections, including some data augmentation. Since the target of this tool are historical documents, we consider as main region types text regions, separators, images, tables and background - each with their own subclasses, e.g. in the case of text regions, subclasses like header/heading, drop capital, main body text etc. While it would be desirable to detect and classify each of these classes in a granular way, there are also limitations due to having a suitably large and balanced training set. Accordingly, the current version of this tool is focussed on the main region types background, text region, image and separator.

Textline detection

In a subsequent step, binary pixel-wise segmentation is used again to classify pixels in a document that constitute textlines. For textline segmentation, a model was initially trained on documents with only one column/block of text and some augmentation with regard to scaling. By fine-tuning the parameters also for multi-column documents, additional training data was produced that resulted in a much more robust textline detection model.

Image enhancement

This is an image to image model which input was low quality of an image and label was actually the original image. For this one we did not have any GT, so we decreased the quality of documents in SBB and then feed them into model.

Scale classification

This is simply an image classifier which classifies images based on their scales or better to say based on their number of columns.

Heuristic methods

Some heuristic methods are also employed to further improve the model predictions:

After border detection, the largest contour is determined by a bounding box, and the image cropped to these coordinates.
For text region detection, the image is scaled up to make it easier for the model to detect background space between text regions.
A minimum area is defined for text regions in relation to the overall image dimensions, so that very small regions that are noise can be filtered out.
Deskewing is applied on the text region level (due to regions having different degrees of skew) in order to improve the textline segmentation result.
After deskewing, a calculation of the pixel distribution on the X-axis allows the separation of textlines (foreground) and background pixels.
Finally, using the derived coordinates, bounding boxes are determined for each textline.

Installation

pip install . or

pip install . -e for editable installation

Alternatively, you can also use make with these targets:

make install or

make install-dev for editable installation

Models

In order to run this tool you also need trained models. You can download our pretrained models from qurator-data.de.

Alternatively, running make models will download and extract models to $(PWD)/models_eynollah.

Usage

The basic command-line interface can be called like this:

eynollah \
-i <image file name> \
-o <directory to write output xml or enhanced image> \
-m <directory of models> \
-fl <if true, the tool will perform full layout analysis> \
-ae <if true, the tool will resize and enhance the image and produce the resulting image as output> \
-as <if true, the tool will check whether the document needs rescaling or not> \
-cl <if true, the tool will extract the contours of curved textlines instead of rectangle bounding boxes> \
-si <if a directory is given here, the tool will output image regions inside documents there>

The tool does accept and works better on original images (RGB format) than binarized images.

`--full-layout` vs `--no-full-layout`

Here are the difference in elements detected depending on the --full-layout/--no-full-layout command line flags:

	`--full-layout`	`--no-full-layout`
reading order	x	x
header regions	x	-
text regions	x	x
text regions / text line	x	x
drop-capitals	x	-
marginals	x	x
marginals / text line	x	x
image region	x	x

How to use

First, this model makes use of up to 9 trained models which are responsible for different operations like size detection, column classification, image enhancement, page extraction, main layout detection, full layout detection and textline detection.That does not mean that all 9 models are always required for every document. Based on the document characteristics and parameters specified, different scenarios can be applied.

If none of the parameters is set to true, the tool will perform a layout detection of main regions (background, text, images, separators and marginals). An advantage of this tool is that it tries to extract main text regions separately as much as possible.
If you set -ae (allow image enhancement) parameter to true, the tool will first check the ppi (pixel-per-inch) of the image and when it is less than 300, the tool will resize it and only then image enhancement will occur. Image enhancement can also take place without this option, but by setting this option to true, the layout xml data (e.g. coordinates) will be based on the resized and enhanced image instead of the original image.
For some documents, while the quality is good, their scale is very large, and the performance of tool decreases. In such cases you can set -as (allow scaling) to true. With this option enabled, the tool will try to rescale the image and only then the layout detection process will begin.
If you care about drop capitals (initials) and headings, you can set -fl (full layout) to true. With this setting, the tool can currently distinguish 7 document layout classes/elements.
In cases where the document includes curved headers or curved lines, rectangular bounding boxes for textlines will not be a great option. In such cases it is strongly recommended setting the flag -cl (curved lines) to true to find contours of curved lines instead of rectangular bounding boxes. Be advised that enabling this option increases the processing time of the tool.
To crop and save image regions inside the document, set the parameter -si (save images) to true and provide a directory path to store the extracted images.
This tool is actively being developed. If problems occur, or the performance does not meet your expectations, we welcome your feedback via issues.

Comments

trying to get running...

Hi. I am trying to get this running on Windows 10 using Visual Studio Code.

If cd into the repo and run a command like: eynollah -i C:/Users/Scott/Desktop/Python2/Kpages/Pages/076v.jpg -o C:/Users/Scott/Desktop/Python2/Kpages -m C:/Users/Scott/Desktop/Python2/eynollah/models_eynollah -si C:/Users/Scott/Desktop/Python2/Kpages it doesn't appear to run. A new command prompt comes up after a couple of seconds -- but no output and no error message.

Any guidance would be appreciated.

opened by SB2020-eye 53
Eynollah on Python 3.8

Hi, Eynollah's requirements include Tensorflow < 2. This option is not suppored on Python 3.8+. It will work on 3.7, but I'd prefer not install a dedicated environment for this. Will it break with a newer version? Do you have plans for upgrading it to TF 2.0+? Thank you.
enhancement

opened by nacho-pancho 18

Receiving error "TypeError: can't pickle _thread.RLock objects"

Hi I am excited trying out your code and I installed it on my Windows 10 machine (Ryzen 3700x cpu, Nvidia RTX 2070 Super gpu) under anaconda (python 3.6.15, tensorflow 2.6.2, cudatoolkit 11.2.2) and it gets pretty far along before it crashes.
Here is my command line...

eynollah --image sn98062568_1933-11-18_ed-1_seq-3.png --out test1 --model models_eynollah --save_layout test1 --full-layout --enable-plotting --allow-enhancement --allow_scaling --log-level DEBUG

And I get sn98062568_1933-11-18_ed-1_seq-3_enhanced.png and sn98062568_1933-11-18_ed-1_seq-3_layout_main.png images generated that look reasonable. But here is the output stream just before and including the error...

14:32:25.982 INFO eynollah - detection of marginals took 4.2s
14:32:25.982 DEBUG eynollah - enter run_boxes_full_layout
14:32:26.780 DEBUG eynollah - enter extract_text_regions
14:32:26.894 DEBUG eynollah - enter start_new_session_and_model (model_dir=models_eynollah/model_3up_new_good_no_augmentation.h5)
14:32:28.952 DEBUG eynollah - enter do_prediction
14:32:28.954 DEBUG eynollah - Patch size: 896x896
14:32:32.797 DEBUG eynollah - enter do_prediction
14:32:32.799 DEBUG eynollah - Patch size: 896x896
14:32:41.277 DEBUG eynollah - exit extract_text_regions
14:32:42.255 DEBUG eynollah - enter extract_text_regions
14:32:42.256 DEBUG eynollah - enter start_new_session_and_model (model_dir=models_eynollah/model_no_patches_class0_30eopch.h5)
14:32:44.120 DEBUG eynollah - enter do_prediction
14:32:45.507 DEBUG eynollah - exit extract_text_regions
14:32:46.658 DEBUG eynollah - exit run_boxes_full_layout
14:33:52.914 DEBUG eynollah - enter get_slopes_and_deskew_new
Traceback (most recent call last):
Traceback (most recent call last):
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\runpy.py", line 193, in _run_module_as_main
  File "<string>", line 1, in <module>
    "__main__", mod_spec)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\multiprocessing\spawn.py", line 105, in spawn_main
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\runpy.py", line 85, in _run_code
    exitcode = _main(fd)
    exec(code, run_globals)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\multiprocessing\spawn.py", line 115, in _main
  File "C:\Users\Steve\anaconda3\envs\qurator-spk\Scripts\eynollah.exe\__main__.py", line 7, in <module>
    self = reduction.pickle.load(from_parent)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\site-packages\click\core.py", line 1128, in __call__
EOFError: Ran out of input
    return self.main(*args, **kwargs)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\site-packages\click\core.py", line 1053, in main
    rv = self.invoke(ctx)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\site-packages\click\core.py", line 1395, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\site-packages\click\core.py", line 754, in invoke
    return __callback(*args, **kwargs)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\site-packages\qurator\eynollah\cli.py", line 151, in main
    pcgts = eynollah.run()
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\site-packages\qurator\eynollah\eynollah.py", line 2458, in run
    slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\site-packages\qurator\eynollah\eynollah.py", line 828, in get_slopes_and_deskew_new
    processes[i].start()
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)
  File "c:\users\steve\anaconda3\envs\qurator-spk\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
TypeError: can't pickle _thread.RLock objects

Do you have any idea of what the problem may be, and what I can do to fix it? Thanks!

opened by sjscotti 17

Documentation: Should the OCR-D processor run on RGB or binarized images?

Should the OCR-D processor run on a RGB or a binarized image input group?

I think it would be best if the README listed an example, e.g.:

ocrd-eynollah-segment -I <WHICH ONE?> -O SEG-LINE -P xyz abc
documentation

opened by mikegerber 14
Running results in OCR-D
Hello again. :)

In this closed issue, @kba kindly recommended the following workflow to use eynollah results in an OCR-D workflow:

ocrd workspace init ocrd workspace add -G IMG -i IMG_1 -g page1 image1.png ocrd workspace add -G SEG -i SEG_1 -g page1 image1.xml ocrd-tesserocr-recognize -P segmentation_level none -P textequiv_level line

I'm having some challenges implementing this. It may just have to do with folders and paths, or maybe some "blanks" I failed to fill in...

Everything goes smoothly until the last line. (I believe it wants an input parameter?) The output is:

Input fileGrp[@USE='INPUT'] not in METS!

If I try adding -I SEG, output includes the following:

Traceback (most recent call last): File "/home/scott/src/github/OCR-D/ocrd_all/venv/lib/python3.6/site-packages/ocrd/workspace.py", line 111, in download_file raise Exception("Not already downloaded, moving on") Exception: Not already downloaded, moving on

and FileNotFoundError: File path passed as 'url' to download_to_directory does not exist: C:/Users/Scott/Desktop/Python2/K/eyn_test2/F073r.jpg and FileNotFoundError: File path passed as 'url' to download_to_directory does not exist: /mnt/c/users/scott/desktop/python2/k/eyn_test2/C:/Users/Scott/Desktop/Python2/K/eyn_test2/F073r.jpg and Exception: Already tried prepending baseurl '/mnt/c/users/scott/desktop/python2/k/eyn_test2'. Cannot retrieve '/mnt/c/users/scott/desktop/python2/k/eyn_test2/C:/Users/Scott/Desktop/Python2/K/eyn_test2/F073r.jpg'

If I try adding -I SEG_1, the output is:

Input fileGrp[@USE='SEG_1'] not in METS!

Any suggestions welcome and appreciated!
opened by SB2020-eye 14

Reverse text line order from OCR-D

Hi, using eynollah in a OCR-D workflow produced a reverse text line order within each region, so that the last actual line is line_001 in the PAGE XML.

I'm new to eynollah and OCR-D, so I might have made a mistake somewhere. Any ideas anyone? Thanks!

I used this workflow:

ocrd process \
  "sbb-binarize -I OCR-D-IMG -O OCR-D-BIN -P model default" \
  "eynollah-segment -I OCR-D-BIN -O OCR-D-SEG -P models default -P curved_line true" \
  "calamari-recognize -I OCR-D-SEG -O OCR-D-OCR -P checkpoint_dir qurator-gt4histocr-1.0"

Used image

PageView screenshot

And here's the xml section corresponding to the first news paragraph:

XML

 <pc:TextRegion id="region_0010" type="paragraph">
<pc:Coords points="372,501 371,501 363,501 363,502 357,502 356,501 350,501 349,502 347,502 347,503 345,505 345,506 345,506 345,520 345,521 345,524 348,526 348,529 361,529 361,528 364,528 365,529 373,529 373,530 373,530 374,531 373,531 372,531 371,532 360,532 360,533 359,533 358,532 348,532 348,533 346,533 345,533 343,533 342,534 341,534 341,535 327,535 327,539 326,540 321,540 321,539 318,539 317,538 316,538 315,538 313,538 313,537 311,537 310,536 309,536 308,536 306,536 305,535 303,535 302,535 294,535 293,535 291,535 290,535 284,535 283,535 281,535 281,535 276,535 275,535 271,535 271,536 263,536 262,536 251,536 250,536 235,536 234,535 224,535 223,536 208,536 207,535 204,535 203,535 181,535 180,535 172,535 171,536 165,536 165,536 165,537 164,536 159,536 158,537 158,537 157,536 156,536 155,536 153,536 153,535 127,535 126,536 118,536 117,535 106,535 105,536 102,536 101,536 101,537 101,538 101,540 100,541 100,542 99,543 99,544 98,545 98,551 99,551 99,555 97,556 96,556 96,557 95,557 94,558 92,558 91,558 90,558 89,559 81,559 80,560 78,560 78,560 76,560 75,561 73,561 72,561 66,561 65,562 60,562 59,563 51,563 50,564 50,574 50,575 50,621 50,621 50,665 49,665 49,681 50,681 50,701 50,701 51,702 51,712 51,713 51,718 52,719 51,720 51,726 52,727 52,731 51,731 51,735 51,735 51,753 51,753 51,757 51,758 51,766 51,767 51,779 52,780 52,795 53,796 53,808 52,809 52,819 53,820 53,830 52,830 52,832 51,833 51,840 52,840 52,850 53,850 53,881 53,881 53,882 53,890 53,890 53,903 53,904 53,909 53,910 53,910 54,910 55,911 127,911 128,911 132,911 133,912 133,912 134,913 138,913 138,913 143,913 144,913 152,913 153,913 155,913 156,914 158,914 159,915 190,915 190,914 205,914 205,915 211,915 212,915 218,915 219,915 224,915 225,913 226,913 227,913 232,913 233,912 235,912 236,911 240,911 240,911 250,911 250,910 270,910 271,910 271,905 271,904 271,898 272,898 272,895 273,894 273,893 273,893 273,892 275,890 276,890 276,890 283,890 283,889 286,889 286,888 288,888 288,888 290,888 290,887 290,886 296,886 296,886 297,886 298,885 302,885 303,886 305,886 306,886 313,886 314,886 323,886 323,885 328,885 328,885 329,885 332,885 333,886 354,886 355,885 357,885 358,885 377,885 378,885 379,885 380,886 394,886 395,885 396,885 396,885 404,885 405,885 410,885 410,886 418,886 419,886 419,888 420,888 428,888 428,888 429,888 429,886 430,886 430,886 431,885 444,885 445,884 452,884 453,885 471,885 471,884 473,884 474,883 478,883 479,884 496,884 496,883 563,883 563,883 565,883 566,883 578,883 578,884 580,884 580,885 591,885 591,884 595,884 596,883 608,883 608,883 633,883 633,883 635,883 635,884 641,884 642,883 650,883 651,883 658,883 658,883 661,883 662,885 663,885 664,886 685,886 686,885 686,885 686,883 687,883 687,880 688,880 688,858 687,858 687,848 686,847 686,841 687,841 687,787 686,786 686,771 686,771 686,769 686,768 686,765 687,765 687,731 686,731 686,725 686,725 686,720 686,719 686,706 686,706 686,698 686,698 686,690 687,689 687,676 686,675 686,668 686,668 686,666 686,666 686,650 686,649 686,633 685,633 685,619 686,618 686,609 685,608 685,598 685,598 685,591 685,590 685,578 685,578 685,541 684,540 684,540 683,539 683,538 683,538 682,538 675,538 675,537 675,536 674,535 674,533 673,532 673,531 666,531 665,532 660,532 660,533 658,533 657,532 654,532 653,531 636,531 635,532 606,532 606,533 603,533 603,532 598,532 598,531 586,531 586,531 581,531 581,530 576,530 576,530 575,530 575,529 575,528 576,528 576,528 577,528 578,527 581,527 581,526 587,526 588,526 594,526 595,525 596,525 597,525 600,525 601,524 601,524 601,523 602,523 630,523 630,522 631,521 642,521 643,522 648,522 648,521 658,521 658,516 657,516 657,515 656,514 655,514 653,512 653,510 653,510 653,508 652,508 634,508 633,507 633,506 632,505 632,503 631,503 626,503 626,503 601,503 600,504 588,504 588,503 583,503 583,503 570,503 570,503 553,503 552,503 532,503 531,503 493,503 493,503 486,503 486,503 469,503 468,504 463,504 463,503 453,503 453,504 440,504 439,503 435,503 434,503 400,503 400,502 392,502 391,501 390,501 390,501"/>
            <pc:TextLine id="region_0010_line_0001">
                <pc:Coords points="55,888 55,888 53,888 52,889 52,890 51,890 51,893 51,893 48,893 48,898 48,899 48,910 49,910 50,910 50,913 50,913 51,914 64,914 65,913 159,913 160,915 160,916 161,916 161,917 176,917 176,916 180,916 181,916 181,915 182,914 183,914 184,913 191,913 191,914 211,914 211,915 223,915 225,913 248,913 249,913 266,913 266,912 268,911 268,911 269,910 276,910 276,910 276,909 276,894 268,894 266,893 251,893 250,892 250,892 249,891 249,891 248,890 248,889 227,889 226,888 226,888 207,888 206,888 203,888 202,889 195,889 195,890 194,889 184,889 183,888 171,888 170,890 160,890 160,891 159,891 158,891 158,892 158,893 135,893 135,893 134,893 133,892 133,891 132,891 132,889 131,888 118,888 118,889 118,890 117,891 98,891 97,890 97,890 96,889 96,888 73,888 72,888 55,888"/>
                <pc:TextEquiv conf="0.996653318405151">
                    <pc:Unicode>Kulturkampfes halten.</pc:Unicode>
                </pc:TextEquiv>
            </pc:TextLine>
            <pc:TextLine id="region_0010_line_0002">
                <pc:Coords points="655,859 654,860 622,860 622,863 621,865 611,865 610,865 583,865 581,863 581,863 580,863 573,863 572,861 572,861 571,860 558,860 558,863 556,865 556,865 555,865 516,865 515,865 515,865 513,863 512,863 511,863 504,863 503,861 474,861 473,863 473,863 472,864 472,865 471,866 412,866 411,865 411,862 390,862 389,863 389,865 386,867 300,867 298,866 298,863 276,863 276,864 276,865 276,866 275,867 270,867 269,868 256,868 256,867 252,867 251,866 251,863 233,863 233,863 231,865 223,865 223,866 216,866 215,866 213,866 213,867 210,867 208,865 208,862 185,862 184,863 157,863 157,865 155,866 153,866 152,866 146,866 145,865 130,865 129,866 129,867 128,868 88,868 88,868 87,868 86,867 86,866 86,866 86,863 50,863 50,863 49,863 49,868 48,868 48,868 48,885 49,885 50,885 50,886 51,888 51,888 53,889 65,889 66,888 92,888 93,888 116,888 116,888 190,888 190,888 202,888 203,888 240,888 240,888 253,888 253,888 260,888 261,888 287,888 288,888 292,888 293,887 415,887 415,888 415,888 416,889 416,890 417,891 431,891 431,891 431,890 432,889 432,888 433,887 435,887 435,886 457,886 458,886 475,886 475,886 501,886 501,886 583,886 583,885 595,885 596,885 597,885 598,885 625,885 626,885 646,885 647,884 656,884 656,885 687,885 688,884 689,884 689,881 690,881 690,861 689,861 683,861 683,860 670,860 670,860 667,860 666,859 655,859"/>
                <pc:TextEquiv conf="0.99672269821167">
                    <pc:Unicode>haben, weil ſie ihn für einen Gegner Bismarcks und des</pc:Unicode>
                </pc:TextEquiv>
            </pc:TextLine>
            <pc:TextLine id="region_0010_line_0003">
                <pc:Coords points="541,835 540,836 540,837 538,839 528,839 527,840 482,840 481,839 468,839 468,840 466,841 443,841 443,841 410,841 410,841 401,841 400,842 384,842 383,841 360,841 358,840 358,838 330,838 330,838 329,839 329,840 328,841 328,841 327,841 313,841 312,842 308,842 308,843 298,843 298,842 284,842 283,841 274,841 273,840 273,840 268,840 267,838 267,838 266,837 211,837 210,838 198,838 197,837 181,837 180,838 160,838 159,839 159,841 158,842 158,843 158,843 156,843 155,843 123,843 122,841 122,840 121,839 121,838 91,838 91,839 90,840 89,840 89,841 88,842 71,842 71,843 65,843 64,842 63,842 62,841 62,838 49,838 49,843 48,843 47,843 47,855 48,855 48,860 49,860 50,860 50,861 53,861 54,862 92,862 93,863 107,863 108,862 123,862 123,863 156,863 157,863 159,863 160,864 180,864 181,863 195,863 195,862 211,862 212,863 234,863 235,863 246,863 248,862 310,862 310,863 310,863 311,864 311,865 311,866 351,866 351,865 352,865 352,864 354,862 388,862 389,861 433,861 435,863 435,864 451,864 451,863 451,863 453,861 456,861 456,861 469,861 470,860 475,860 475,861 500,861 501,860 539,860 540,861 551,861 552,860 560,860 560,860 612,860 613,859 620,859 621,860 636,860 637,859 674,859 675,858 686,858 688,856 690,856 690,840 688,840 687,840 686,840 685,838 685,838 684,838 671,838 671,838 666,838 666,839 641,839 640,837 640,836 618,836 615,840 580,840 579,840 577,840 576,840 568,840 567,838 567,836 566,835 541,835"/>
                <pc:TextEquiv conf="0.995206952095032">
                    <pc:Unicode>bereiteten Feierlichkeiten zeigten, mag darin ſeinen Grund</pc:Unicode>
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                    <pc:Unicode>Schwarzen ſich weniger zurückhaltend bei den dem Kronprinzen</pc:Unicode>
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                    <pc:Unicode>allenthalben einen ſympathiſchen Empfang. Daß auch die</pc:Unicode>
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                    <pc:Unicode>Reiches. der in Bahern mehrere Truppenrevüen abhielt, fand</pc:Unicode>
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                    <pc:Unicode>gendſten Truppeninſpektionen vor. Der Kronprinz des Deutſchen</pc:Unicode>
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                    <pc:Unicode>ſich des veſten Wohlſeins und nimmt noch häufig die anſtren—</pc:Unicode>
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                    <pc:Unicode>ſehen und begünſtigen. Se. M. der Deutſche Kaiſer erfreut</pc:Unicode>
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                    <pc:Unicode>höheren geiſtlichen Behörden ſolche Vorpoftengefechte gerne</pc:Unicode>
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                    <pc:Unicode>der Tagesordnung und werden ſolange vorkommen, als die</pc:Unicode>
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                    <pc:Unicode>ſperrungen zelotiſcher Hetzkapläne ſtehen auch jetzt noch auf</pc:Unicode>
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                    <pc:Unicode>d. h. Nachrichten von größerem Belange, denn kleine Ein—</pc:Unicode>
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            </pc:TextLine>
            <pc:TextLine id="region_0010_line_0014">
                <pc:Coords points="346,557 345,558 333,558 333,558 333,559 332,560 332,561 331,562 330,562 329,563 296,563 295,562 295,562 293,561 293,560 293,559 269,559 268,558 254,558 254,561 252,563 226,563 226,562 225,562 224,561 223,561 223,561 223,560 211,560 211,561 210,561 203,561 202,562 197,562 196,563 171,563 170,563 140,563 140,563 139,563 138,562 138,561 138,561 138,560 137,559 135,559 135,558 130,558 129,558 82,558 81,558 70,558 69,559 47,559 47,583 48,584 118,584 119,585 131,585 132,583 169,583 170,583 206,583 206,582 232,582 233,583 255,583 256,583 268,583 269,583 271,583 273,585 273,586 285,586 285,585 286,584 286,583 288,582 315,582 315,583 328,583 330,584 331,584 331,585 333,585 333,585 351,585 352,584 352,583 353,582 378,582 378,581 400,581 401,582 427,582 428,581 433,581 434,582 464,582 465,581 465,581 466,582 467,582 468,583 468,583 468,584 468,585 485,585 485,584 485,583 486,581 521,581 522,581 558,581 559,580 570,580 571,581 583,581 585,583 601,583 601,582 603,581 606,581 607,580 636,580 636,580 668,580 668,580 683,580 683,578 683,578 690,578 690,561 681,561 680,560 669,560 668,560 657,560 656,559 644,559 643,560 642,560 641,560 589,560 588,561 523,561 521,560 510,560 508,561 500,561 500,561 491,561 491,561 485,561 485,560 483,560 483,560 481,560 480,558 480,558 465,558 464,558 455,558 455,559 438,559 438,560 438,560 436,561 418,561 416,559 400,559 400,560 400,561 399,561 398,560 378,560 378,561 376,561 375,560 375,558 375,558 368,558 368,557 346,557"/>
                <pc:TextEquiv conf="0.993075370788574">
                    <pc:Unicode>Nachrichten in der letzten Zeit etwas ſparſamer geworden,</pc:Unicode>
                </pc:TextEquiv>
            </pc:TextLine>
            <pc:TextLine id="region_0010_line_0015">
                <pc:Coords points="658,528 658,529 655,529 655,530 630,530 629,529 597,529 596,530 596,532 595,533 595,535 595,535 555,535 555,535 555,531 531,531 531,530 503,530 502,531 502,533 501,533 493,533 491,535 485,535 485,536 462,536 461,535 461,532 461,531 460,531 460,531 446,531 445,531 445,531 444,532 444,535 443,536 441,536 441,536 406,536 405,536 404,536 403,535 403,532 403,531 359,531 358,533 358,535 357,536 356,536 355,537 309,537 308,536 306,536 305,536 305,531 273,531 271,533 270,533 270,533 263,533 263,534 263,535 262,535 261,536 258,536 258,536 258,537 226,537 225,536 211,536 210,536 210,537 210,538 177,538 176,537 175,537 175,536 162,536 160,538 142,538 141,538 124,538 123,536 122,536 121,536 121,533 121,532 104,532 103,533 101,533 101,534 100,535 97,535 96,536 95,536 95,537 94,538 94,539 93,540 93,554 103,554 103,555 103,555 104,556 105,556 105,556 131,556 132,557 132,558 160,558 161,557 162,557 163,558 205,558 205,557 261,557 262,558 271,558 273,560 273,560 305,560 305,560 308,557 318,557 319,556 323,556 323,557 340,557 341,556 358,556 359,557 397,557 398,556 423,556 424,556 440,556 440,556 490,556 491,558 491,559 492,560 520,560 520,559 520,558 521,556 522,556 523,556 539,556 540,555 594,555 595,556 607,556 608,555 635,555 635,555 645,555 646,554 655,554 656,555 681,555 681,554 686,554 686,553 686,551 687,550 690,550 690,536 689,535 689,535 688,534 678,534 677,533 677,529 671,529 671,528 658,528"/>
                <pc:TextEquiv conf="0.975517272949219">
                    <pc:Unicode>Von den deutfchen Cultur-Kampfſtätten ſind die</pc:Unicode>
                </pc:TextEquiv>
            </pc:TextLine>
            <pc:TextLine id="region_0010_line_0016">
                <pc:Coords points="549,500 548,500 543,500 542,501 516,501 515,501 515,502 515,503 514,503 514,503 513,504 513,505 513,505 510,505 510,506 482,506 481,505 480,505 480,505 479,505 478,504 466,504 465,505 465,505 464,506 451,506 450,505 436,505 435,504 435,501 404,501 403,501 390,501 389,501 375,501 375,501 349,501 348,502 348,505 348,506 345,506 345,506 340,506 340,523 346,523 346,523 346,528 360,528 361,527 386,527 387,526 406,526 406,527 418,527 419,526 420,526 420,526 460,526 461,525 467,525 468,526 487,526 488,525 571,525 571,526 583,526 584,525 598,525 598,525 656,525 656,522 656,521 663,521 663,511 662,511 662,510 661,509 660,509 658,507 658,505 656,505 655,505 646,505 645,504 636,504 636,503 636,502 635,501 635,500 622,500 621,500 621,500 620,501 620,503 619,504 596,504 596,503 591,503 591,503 588,503 588,502 578,502 577,501 570,501 568,500 549,500"/>
                <pc:TextEquiv conf="0.9727663397789">
                    <pc:Unicode>Roſenheim, den 5. September.</pc:Unicode>
                </pc:TextEquiv>
            </pc:TextLine>
            <pc:TextEquiv>
                <pc:Unicode>Kulturkampfes halten.
haben, weil ſie ihn für einen Gegner Bismarcks und des
bereiteten Feierlichkeiten zeigten, mag darin ſeinen Grund
Schwarzen ſich weniger zurückhaltend bei den dem Kronprinzen
allenthalben einen ſympathiſchen Empfang. Daß auch die
Reiches. der in Bahern mehrere Truppenrevüen abhielt, fand
gendſten Truppeninſpektionen vor. Der Kronprinz des Deutſchen
ſich des veſten Wohlſeins und nimmt noch häufig die anſtren—
ſehen und begünſtigen. Se. M. der Deutſche Kaiſer erfreut
höheren geiſtlichen Behörden ſolche Vorpoftengefechte gerne
der Tagesordnung und werden ſolange vorkommen, als die
ſperrungen zelotiſcher Hetzkapläne ſtehen auch jetzt noch auf
d. h. Nachrichten von größerem Belange, denn kleine Ein—
Nachrichten in der letzten Zeit etwas ſparſamer geworden,
Von den deutfchen Cultur-Kampfſtätten ſind die
Roſenheim, den 5. September.</pc:Unicode>
            </pc:TextEquiv>

bug

opened by aurichje 13

IndexError: list index out of range (slopes[region_idx])

Hi, when running Eynollah on this image, using

en = Eynollah("models_eynollah", imgfile, dir_out=path.split(imgfile)[0], curved_line=True, full_layout=True)
pcgts = en.run()

it fails with

Traceback (most recent call last):
  File "<stdin>", line 6, in <module>
  File "venv/lib/python3.6/site-packages/qurator/eynollah/eynollah.py", line 2074, in run
    pcgts = self.writer.build_pagexml_full_layout(contours_only_text_parent, contours_only_text_parent_h, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, polygons_of_images, polygons_of_tabels, polygons_of_drop_capitals, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml)
  File "venv/lib/python3.6/site-packages/qurator/eynollah/writer.py", line 221, in build_pagexml_full_layout
    self.serialize_lines_in_region(textregion, all_found_texline_polygons_h, mm, page_coord, all_box_coord_h, slopes, counter)
  File "venv/lib/python3.6/site-packages/qurator/eynollah/writer.py", line 117, in serialize_lines_in_region
    if self.curved_line and np.abs(slopes[region_idx]) <= 45:
IndexError: list index out of range

Some other pages from the book seem to work, the results are looking really good (except for drop caps, but they are not that easy to identify and put in the correct order for humans as well). I'm segmenting the rest of the book now and will see if there are more errors like that one.

opened by andbue 9

Irritating "Image dimension" log message
I am processing a 4000x6000 image using ocrd-eynollah-segment and get - among other messages - this message:

14:32:10.541 INFO eynollah - Image dimensions: 448x672

Should this read "Patch dimensions" and maybe get a log level of DEBUG?
documentation
opened by mikegerber 8

drop_capitals.py: ValueError: attempt to get argmin of an empty sequence

Hi, I think I found another one:

wget https://api.digitale-sammlungen.de/iiif/image/v2/bsb00052981_00339/full/full/0/default.png
eynollah -i default.png -o . -m eynollah/models_eynollah -fl -cl

13:16:03.204 INFO eynollah - resize and enhance image
13:16:03.204 INFO eynollah - Detected 230 DPI
13:16:19.326 INFO eynollah - Found 3 columns ([[1.6621375e-26 1.6978607e-38 1.0000000e+00 2.5424867e-32 9.4024474e-31
  0.0000000e+00]])
13:16:33.584 INFO eynollah - Image is enhanced
13:16:33.726 INFO eynollah - Enhancing took 30.522119998931885s
13:16:39.280 INFO eynollah - Image dimensions: 448x672
13:16:58.684 INFO eynollah - Image dimensions: 448x672
13:17:19.415 INFO eynollah - Image dimensions: 448x672
13:17:39.792 INFO eynollah - ratio_of_two_models: 99.93604678448163
13:17:40.588 INFO eynollah - Textregion detection took 66.86148571968079s
13:17:47.636 INFO eynollah - Graphics detection took 7.048167943954468s
13:17:47.636 INFO eynollah - cont_page [array([[  88,   87],
       [2933,   87],
       [2933, 4525],
       [  88, 4525]])]
13:17:52.956 INFO eynollah - Image dimensions: 448x672
13:18:04.696 INFO eynollah - textline detection took 17.060104370117188s
13:18:21.939 INFO eynollah - slope_deskew: -0.3636363636363633
13:18:21.939 INFO eynollah - deskewing took 17.242716073989868s
13:18:21.962 INFO eynollah - detection of marginals took 0.022979736328125s
13:18:27.893 INFO eynollah - Image dimensions: 896x896
13:18:33.513 INFO eynollah - Image dimensions: 896x896
13:18:53.899 INFO eynollah - areas_cnt_text [6.06679334e-05 3.96004787e-08 1.24939510e-03 1.53873996e-02
 3.28577052e-03 5.43809614e-03 5.36713208e-03 6.94196391e-05
 1.72341283e-04 1.30660354e-01 1.54637414e-01 7.77194243e-02
 3.97628407e-04 1.18769756e-03 4.26853560e-04]
Traceback (most recent call last):
  File "/.../bin/eynollah", line 33, in <module>
  File "/.../lib/python3.7/site-packages/click/core.py", line 1137, in __call__
    return self.main(*args, **kwargs)
  File "/.../lib/python3.7/site-packages/click/core.py", line 1062, in main
    rv = self.invoke(ctx)
  File "/.../lib/python3.7/site-packages/click/core.py", line 1404, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/.../lib/python3.7/site-packages/click/core.py", line 763, in invoke
    return __callback(*args, **kwargs)
  File "/.../lib/python3.7/site-packages/qurator/eynollah/cli.py", line 142, in main
    pcgts = eynollah.run()
  File "/.../lib/python3.7/site-packages/qurator/eynollah/eynollah.py", line 2024, in run
    all_found_texline_polygons = adhere_drop_capital_region_into_corresponding_textline(text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, kernel=KERNEL, curved_line=self.curved_line)
  File "/.../lib/python3.7/site-packages/qurator/eynollah/utils/drop_capitals.py", line 157, in adhere_drop_capital_region_into_corresponding_textline
    arg_min = np.argmin(np.abs(y_lines - y_min_d[i_drop]))
  File "<__array_function__ internals>", line 6, in argmin
  File "/.../lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 1267, in argmin
    return _wrapfunc(a, 'argmin', axis=axis, out=out)
  File "/.../lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 61, in _wrapfunc
    return bound(*args, **kwds)
ValueError: attempt to get argmin of an empty sequence

opened by andbue 6

Unable to process document due to ValueError: attempt to get argmax of an empty sequence

For this workspace:

PPN729186350.zip

I get the following error:


Traceback (most recent call last):
  File "/usr/local/bin/ocrd-eynollah-segment", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.6/dist-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/usr/local/lib/python3.6/dist-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/usr/local/lib/python3.6/dist-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/qurator/eynollah/ocrd_cli.py", line 8, in main
    return ocrd_cli_wrap_processor(EynollahProcessor, *args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/ocrd/decorators/__init__.py", line 91, in ocrd_cli_wrap_processor
    run_processor(processorClass, ocrd_tool, mets, workspace=workspace, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/ocrd/processor/helpers.py", line 72, in run_processor
    processor.process()
  File "/usr/local/lib/python3.6/dist-packages/qurator/eynollah/processor.py", line 57, in process
    Eynollah(**eynollah_kwargs).run()
  File "/usr/local/lib/python3.6/dist-packages/qurator/eynollah/eynollah.py", line 1744, in run
    contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
  File "<__array_function__ internals>", line 6, in argmax
  File "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py", line 1186, in argmax
    return _wrapfunc(a, 'argmax', axis=axis, out=out)
  File "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py", line 61, in _wrapfunc
    return bound(*args, **kwds)
ValueError: attempt to get argmax of an empty sequence

Command line used:

ocrd-eynollah-segment --overwrite -I OCR-D-IMG-BIN -O OCR-D-SEG-LINE -P models /var/lib/eynollah

opened by mikegerber 6

How to get Image segment as img as Shown in repo for newspaper cutting

Hi Sir, Can you pls tell the way I can get the result as segmented image that has mapped layouts with different color present in image. As the example you have displayed on the repo-https://user-images.githubusercontent.com/952378/102350683-8a74db80-3fa5-11eb-8c7e-f743f7d6eae2.jpg. Thankyou
documentation

opened by dhirendraAL 6
ValueError: bad marshal data

I am using Ubuntu 20.04 Linux

conda create -n eynollah -y python=3.8 conda activate eynollah // in eynollah's directory pip install -e . make models

// execute following command generates "ValueError: bad marshal data" eynollah -i data/input/1.jpg -o data/output -m models_eynollah

Full error messages is below.

Traceback (most recent call last): File "/anaconda/envs/eynollah/bin/eynollah", line 33, in sys.exit(load_entry_point('eynollah', 'console_scripts', 'eynollah')()) File "/anaconda/envs/eynollah/lib/python3.8/site-packages/click/core.py", line 1130, in call return self.main(*args, **kwargs) File "/anaconda/envs/eynollah/lib/python3.8/site-packages/click/core.py", line 1055, in main rv = self.invoke(ctx) File "/anaconda/envs/eynollah/lib/python3.8/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) File "/anaconda/envs/eynollah/lib/python3.8/site-packages/click/core.py", line 760, in invoke return __callback(*args, **kwargs) File "/home/mylogin/notebooks/eynollah/qurator/eynollah/cli.py", line 151, in main pcgts = eynollah.run() File "/home/mylogin/notebooks/eynollah/qurator/eynollah/eynollah.py", line 2307, in run img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement() File "/home/mylogin/notebooks/eynollah/qurator/eynollah/eynollah.py", line 1990, in run_enhancement is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = self.resize_and_enhance_image_with_column_classifier() File "/home/mylogin/notebooks/eynollah/qurator/eynollah/eynollah.py", line 408, in resize_and_enhance_image_with_column_classifier _, page_coord = self.early_page_for_num_of_column_classification(img_bin) File "/home/mylogin/notebooks/eynollah/qurator/eynollah/eynollah.py", line 648, in early_page_for_num_of_column_classification model_page, session_page = self.start_new_session_and_model(self.model_page_dir) File "/home/mylogin/notebooks/eynollah/qurator/eynollah/eynollah.py", line 518, in start_new_session_and_model model = load_model(model_dir, compile=False) File "/anaconda/envs/eynollah/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "/anaconda/envs/eynollah/lib/python3.8/site-packages/keras/utils/generic_utils.py", line 103, in func_load code = marshal.loads(raw_code) ValueError: bad marshal data (unknown type code)

opened by YanZhangADS 0
performance with high-res images

Sometimes the input comes with DPI 600 or beyond. It seems to me this makes eynollah become much slower. Larger resolution might be needed for newspapers, but there is always a point at which result quality does increase. I would assume that a single downscaling interpolation after import should not be too costly.

The documentation of allow_scaling says that it would also scale down images. But the implementation does not look like that's the case:

https://github.com/qurator-spk/eynollah/blob/8d5079c909b662eda0b4acf5ae2908455f0ff939/qurator/eynollah/eynollah.py#L437-L444

IIUC only too small images get upsampled. I'd expect a secondary DPI_THRESHOLD2 at which downsampling would begin.

opened by bertsky 2
What is the known working GPU config?

I am using an Amazon pressed Ubuntu 16 Deep Learning AMI which contains CUDA 10, 10.1, 10.2, and 11.

I am using Mambaforge with Python 3.6 or 3.7

Tensorflow 2 is automatically used. I plan to try Tensorflow 1.x next.

The process is loaded into GPU memory, but the GPU is never used.

Is there a known working full stack config for eynollah on the GPU (OS+version, CUDA+version, Python+version, Tensorflow+version, etc) that you don't mind sharing?

Thanks,

opened by centerofexcellence 1

No segmentation results for specific image - (due to detecting 6 columns when there is only 1?)

Hi I have run over 2000 images through eynollah as a OCR-D processor, but only 1 gave me this problem. There was no error detected, but the mets.xml file has no segmentation results. The only thing I know is that the image was detected as having 6 columns when there is only 1 actual column. The data for this case is below. Thanks in advance!

Image processed attached...

bqwndyazflxxtnmszruzzmlyofrxvtzc_s089_1561992285623

OCR-D eynollah command and output to console...

(qurator) D:\qurator>ocrd-eynollah-segment -I OCR-D-IMG -O OCR-D-IMG-SEG -P models eynollah/models_eynollah -P dpi 360 -P allow_scaling true
10:32:03.361 INFO eynollah - INPUT FILE P_00738 (1/1)

10:32:03.809 INFO eynollah - Resizing and enhancing image...
10:32:03.809 INFO eynollah - Detected 360 DPI
1/1 [==============================] - 3s 3s/step
1/1 [==============================] - 1s 646ms/step
10:32:11.003 INFO eynollah - Found 6 columns ([[0.09252959 0.01236904 0.0052445  0.05544147 0.01741231 0.8170032 ]])
10:32:11.003 INFO eynollah - Image was not enhanced.
1/1 [==============================] - 1s 732ms/step
1/1 [==============================] - 1s 628ms/step
10:32:15.064 INFO eynollah - Found 6 columns ([[0.09252959 0.01236904 0.0052445  0.05544147 0.01741231 0.8170032 ]])
1/1 [==============================] - 1s 833ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 22ms/step

...
NOTE: many similar lines
...

1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 24ms/step
1/1 [==============================] - 0s 28ms/step
10:34:32.242 INFO eynollah - Textregion detection took 114.8s
1/1 [==============================] - 1s 733ms/step
10:34:35.931 INFO eynollah - Graphics detection took 3.7s
1/1 [==============================] - 1s 769ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 31ms/step

...
NOTE: many similar lines
...

1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 31ms/step
1/1 [==============================] - 0s 22ms/step
10:34:59.506 INFO eynollah - textline detection took 23.6s
10:36:15.179 INFO eynollah - slope_deskew: -90.0
10:36:15.179 INFO eynollah - deskewing took 75.7s
10:36:15.332 INFO eynollah - detection of marginals took 0.2s
1/1 [==============================] - 2s 2s/step
1/1 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 16ms/step

...
NOTE: many similar lines
...

1/1 [==============================] - 0s 22ms/step
1/1 [==============================] - 0s 31ms/step
1/1 [==============================] - 1s 764ms/step
10:39:20.846 INFO eynollah - Job done in 437.0s
10:39:21.100 INFO ocrd.process.profile - Executing processor 'ocrd-eynollah-segment' took 437.728415s (wall) 813.187500s (CPU)( [--input-file-grp='OCR-D-IMG' --output-file-grp='OCR-D-IMG-SEG' --parameter='{"models": "eynollah/models_eynollah", "dpi": 360, "allow_scaling": true, "full_layout": true, "curved_line": false, "headers_off": false}' --page-id='']
10:39:21.100 INFO ocrd.workspace.save_mets - Saving mets 'D:\qurator\mets.xml'

and below is the contents of the mets.xml file...

<?xml version="1.0" encoding="UTF-8"?>
<mets:mets xmlns:mets="http://www.loc.gov/METS/" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="info:lc/xmlns/premis-v2 http://www.loc.gov/standards/premis/v2/premis-v2-0.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-6.xsd http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mix/v10 http://www.loc.gov/standards/mix/mix10/mix10.xsd">
  <mets:metsHdr CREATEDATE="2022-07-22T10:29:16.958375">
    <mets:agent TYPE="OTHER" OTHERTYPE="SOFTWARE" ROLE="CREATOR">
      <mets:name>ocrd/core v2.34.0</mets:name>
    </mets:agent>
    <mets:agent TYPE="OTHER" OTHERTYPE="SOFTWARE" ROLE="OTHER" OTHERROLE="layout/segmentation/region">
      <mets:name>ocrd-eynollah-segment v0.0.11</mets:name>
      <mets:note xmlns:ocrd="https://ocr-d.de" ocrd:option="input-file-grp">OCR-D-IMG</mets:note>
      <mets:note xmlns:ocrd="https://ocr-d.de" ocrd:option="output-file-grp">OCR-D-IMG-SEG</mets:note>
      <mets:note xmlns:ocrd="https://ocr-d.de" ocrd:option="parameter">{"models": "eynollah/models_eynollah", "dpi": 360, "allow_scaling": true, "full_layout": true, "curved_line": false, "headers_off": false}</mets:note>
      <mets:note xmlns:ocrd="https://ocr-d.de" ocrd:option="page-id"/>
    </mets:agent>
  </mets:metsHdr>
  <mets:dmdSec ID="DMDLOG_0001">
    <mets:mdWrap MDTYPE="MODS">
      <mets:xmlData>
        <mods:mods xmlns:mods="http://www.loc.gov/mods/v3">
          <mods:identifier type="purl">'test'</mods:identifier>
        </mods:mods>
      </mets:xmlData>
    </mets:mdWrap>
  </mets:dmdSec>
  <mets:amdSec ID="AMD">
    </mets:amdSec>
  <mets:fileSec>
    <mets:fileGrp USE="OCR-D-IMG">
      <mets:file ID="OCR-D-IMG_00738" MIMETYPE="image/png">
        <mets:FLocat LOCTYPE="OTHER" OTHERLOCTYPE="FILE" xlink:href="OCR-D-IMG\bqwndyazflxxtnmszruzzmlyofrxvtzc_s089_1561992285623.png"/>
      </mets:file>
    </mets:fileGrp>
    <mets:fileGrp USE="OCR-D-IMG-SEG">
      <mets:file ID="OCR-D-IMG-SEG_00738" MIMETYPE="application/vnd.prima.page+xml">
        <mets:FLocat LOCTYPE="OTHER" OTHERLOCTYPE="FILE" xlink:href="OCR-D-IMG-SEG\OCR-D-IMG-SEG_00738.xml"/>
      </mets:file>
    </mets:fileGrp>
  </mets:fileSec>
  <mets:structMap TYPE="PHYSICAL">
    <mets:div TYPE="physSequence">
      <mets:div TYPE="page" ID="P_00738">
        <mets:fptr FILEID="OCR-D-IMG_00738"/>
        <mets:fptr FILEID="OCR-D-IMG-SEG_00738"/>
      </mets:div>
    </mets:div>
  </mets:structMap>
</mets:mets>

opened by sjscotti 3

Flag for OCR-D processor to periodically save mets.xml file (a suggestion)

Hi I seem to be sporadically crashing eynollah on one of a large number of images when running it as an OCR-D processor. This may happen after a large number of images were processed - which takes many hours to run. Because eynollah currently updates the mets.xml file with the segmentation files created only when the processor completes, all the results from that run are missing from the mets.xml file so an OCR cannot be performed on the successful segmentations. The two alternatives seem to be: 1) debug why eynollah is crashing (or eliminate the image causing the crash) and rerun all the images again, or 2) edit the mets.xml by hand to include the info for the successful segmentations that were done before the crash. Is there another approach that can be used if this case occurs? If not, how about including a flag in the OCR-D processor so that it periodically updates the mets.xml file with the info from the successful segmentations. Thanks!

opened by sjscotti 1

Releases(v0.0.10)

v0.0.10(Sep 27, 2021)
Fixed:

call to build_pagexml_no_full_layout for empty pages, #52

Source code(tar.gz)
Source code(zip)
v0.0.9(Aug 16, 2021)
Added:

Table detection, #48

Fixed:

Catch exception, #47

Source code(tar.gz)
Source code(zip)
v0.0.8(Jul 27, 2021)
Fixed:

pc:PcGts/@pcGtsId was not set, #49

Source code(tar.gz)
Source code(zip)
v0.0.7(Jul 1, 2021)
Fixed:

slopes/slopes_h retval/arguments mixed up, #45, #46

Source code(tar.gz)
Source code(zip)
v0.0.6(Jun 22, 2021)
Fixed:

Cast arguments to opencv2 to python native types, #43, #44, opencv/opencv#20186

Source code(tar.gz)
Source code(zip)
v0.0.5(May 19, 2021)
Changed:

OCR-D processor: Remove allow_enhancement parameter, #42

Source code(tar.gz)
Source code(zip)
v0.0.4(May 18, 2021)
Fixed

fix contour bug, #40

Source code(tar.gz)
Source code(zip)
v0.0.3(May 11, 2021)
Fixed:

NaN bug, #38

Source code(tar.gz)
Source code(zip)
v0.0.2(May 4, 2021)
Fixed:

prevent negative coordinates for textlines in marginals

fix a bug in the contour logic, #8

Source code(tar.gz)
Source code(zip)
v0.0.1(Apr 22, 2021)

Initial release
Source code(tar.gz)
Source code(zip)