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Post Decapod 0.4 examination and notes

Post Decapod 0.4 examination and notes

Aug 11, 2011

0.4 functionality

  • Independent left-right (or L-R) remote capture from a set of supported cameras

  • L-R page ordering and rotation correction

  • L-R image stitching and binarization (same process?)

  • thumbnail generation

  • single image delete and reorder

  • output to type 1 image PDF

Testing genpdf

Using Aug 2, 2011 tip functionality

Observations:

  • automatic deskew correction if conditions are right

    • not sure what conditions need to exist for deskew to occur

    • does not happen with images -> possibly related to detected page geometry?

  • type 2 and 3 pdf generation functional. Quality of detected text not completely accurate (this is expected?).

  • binary version stored in Book Store

  • given a set of images that are handwritten with some colour (example), Type 2 PDF comes out greyscale with no selectable text (example).

  • given a set of images that are strictly colour images produce a Type 1 PDF:

    • decapod-genpdfEXP.py produces an empty PDF.

    • decapod-genpdf.py produces a greyscale PDF .

  • content of output PDFs are fit to A4 by default.

  • For type 3 PDF, genpdf automatically reverts to type 2 if character segmentation fails.

  • For type 1 PDF, binary and paragraph map generation, and line segmentation are always performed.

    • Consequence is that images with no detectable text are subjected to long processing.

  • Some PDF metadata generated - i.e. PDF author is "DECAPOD GenPDF", Producer is "ReportLab http://www.reportlab.com"

Future Improvements & Questions

  • Colour output option: binary, greyscale, and colour.

  • Straight Image PDF generation option (no segmentation requested).

    • Option to disable page & text segmentation.

  • PDF metadata options (user can specify their own metadata).

  • Ability to disable auto-deskew (so auto-deskew can happen earlier in the pipeline and not have to attempt deskew twice).

  • Does upgrading from Ocropus 0.4.4 improve text results? What work will we need to do to support a newer version of ocropus?

  • Is there a fail-quick method of detecting text on a page?

Example Test: Type 2 PDF Test - "Computer Generated 600DPI document"

  • Running: Tip version of Decapod with Ocropus 0.4.4

  • Command run: decapod-genpdfEXP.py

  • using first 10 images of the Nuforc computer generated document.

  • Generated PDF

  • Virtual machine spec: Intel 2.8GHz quad-core, 2GB RAM

  • PDF Generation took 10s.

  • Whole process took 372s.

Example Test: Type 3 PDF Test - "Computer Generated 600DPI document"

  • Running: Tip version of Decapod with Ocropus 0.4.4

  • Command run: decapod-genpdfEXP.py

  • using first 10 images of the Nuforc computer generated document.

  • Generated PDF

  • Virtual machine spec: Intel 2.8GHz quad-core, 2GB RAM

  • PDF Generation took 25s.

  • Whole process took 1097s.

Still to do

More testing of:

  • text detection / recognitionFont generation (type 3 PDF)

  • type 2 generation

  • determining the ideal quality of input image to get best results from genpdf.

  • changing character model may improve results?

General workflow

  1. Acquire images: remote capture, or file import.

  2. Page management and QA

  3. Output files

1. Acquire images

Remote Capture

  • 3 modes of remote capture: structured light, stereo, and L-R.

  • Each mode has its own calibration scheme.

  • Each 3D capture mode has its own dewarp scheme.

Import from file system

  • Reasonable assumptions?

    • Files numbered in sequence.

  • Questions

    • What if images are different resolutions? Unpredictable segmentation results.

    • What if pages are inconsistent sizes? Unpredictable segmentation results.

    • What if images contain a mix of page spreads and single pages? Okay if resolution and page sizes are consistent.

2. Page Management

  • During page management, present thumbnails and images as it would be on output to file.

Image with text case:

  • automatically deskew image

  • automatically binarize image? display images/thumbnails in binary? or we keep it colour and save any colour processing as part of an export "preview"?

Image with no recognizable text case:

  • manual deskew correction for image

Global functions

  • automatically 3D dewarp pages if necessary

  • Crop image(s)

  • Delete image(s)

  • Reorder image(s)

3. Output Files

  • choose colour depth of output: binary or colour (original)

  • choose pdf format: image, overlaid, or scalable.

  • Any way to fail quickly at page segementation? - this way Decapod can switch to image PDF output if it doesn't detect text.