k2pdfopt: optimizes PDFs for viewing on e-readers

[jonnyl]

What is the best way to run k2pdfopt on an Android phone? I'm assuming via Linux? Has anyone successfully been able to get it to work and would have step-by-step instructions?

[techytita]

Good question above. Hoping to get some help here.

[willus]

I don't have a test platform for compiling or running on an Android phone. I might be able to cross-compile, but I'd still need to test it. Is there a virtual Android phone simulator that will run on either a PC or a Mac Mini?

[Jamshid]

Originally Posted by willus

I don't have a test platform for compiling or running on an Android phone. I might be able to cross-compile, but I'd still need to test it. Is there a virtual Android phone simulator that will run on either a PC or a Mac Mini?

There's already a k2pdfopt port for android and last time I tested it, was based on version 2.54.
You should install Termux and install packages k2pdfopt and ghostscript.
Termux may not be available on google play and should be downloaded and installed manually.

[Quoth]

Originally Posted by willus

I don't have a test platform for compiling or running on an Android phone. I might be able to cross-compile, but I'd still need to test it. Is there a virtual Android phone simulator that will run on either a PC or a Mac Mini?

Years ago when I last wrote an Android app the development system ran on Windows or Linux and included a emulated/simulated phone plus a faster than MTP method (ADB?) to load app to also test on an actual Android phone.
All a free download from Google.
It was very like doing a Java app, except Google is a pain and keeps changing how you do I/O to SD card or storage. Really Windows for Workgroups 3.11 was better (apart from lack of Explorer desktop, as long as Win32s, 32 bit TCP/IP and 32 bit Disk etc all added), stupid changes since Android 3 and poor UI. Material Design is idiotic. But then I think Win10/Win11/Chrome/Mozilla UIs are all rubbish compared to mac OS9, Win9x, NT4.0/2K/XP UIs.

[Used1]

Is this where I can ask for help with getting the formatting down? A lot of this is way over my head. And can I post screenshots here to show what I mean?

[j.p.s]

Originally Posted by Used1

Is this where I can ask for help with getting the formatting down? A lot of this is way over my head. And can I post screenshots here to show what I mean?

Yes, but please be patient because I don't think willus visits every day. (Of course, someone else might know answers to your questions.)

You can post screenshots here, but if they are larger than 600x600 please attach them rather than embed them. (Use the "Manage Attachments" tool.) Also, please don't post deep links to images because that frequently causes problems.

[schezuk]

I wrote myself a dirty piece of code to rotate every CJK character by 90 degrees, which is designed to ignore Latin script if a word is wide enough. Not an effective implementation but with many magic numbers, though.

#! /usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import cv2
import numpy

def rotate_image(image, angle):
	(h, w) = image.shape[: 2]
	center = (w // 2, h // 2)
	M = cv2.getRotationMatrix2D(center, angle, 1.0)
	corrected = cv2.warpAffine(image, M, (w, h), flags = cv2.INTER_CUBIC, borderMode = cv2.BORDER_REPLICATE)
	return corrected

def determine_score(arr):
	histogram = numpy.sum(arr, axis = 2, dtype = float)
	score = numpy.sum((histogram[..., 1 :] - histogram[..., : -1]) ** 2, axis = 1, dtype = float)
	return score

def correct_skew(monoImg, delta = 0.1, limit = 5):
	angles = numpy.arange(-limit, limit + delta, delta)
	img_stack = numpy.stack([rotate_image(monoImg, angle) for angle in angles], axis = 0)
	scores = determine_score(img_stack)
	best_angle = angles[numpy.argmax(scores)]
	return best_angle


orgImage = cv2.imread(sys.argv[1])
myImage = numpy.ascontiguousarray(numpy.rot90(orgImage))
(my_h, my_w, _) = myImage.shape

grayImg = cv2.cvtColor(myImage, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(grayImg, (0,0), sigmaX=33, sigmaY=33)
divide = cv2.divide(grayImg, blur, scale=255)
_, myImage = cv2.threshold(divide,127,255,cv2.THRESH_BINARY)
myImage = divide


at_bs = 5
monoImg = cv2.ximgproc.niBlackThreshold(grayImg, 255, cv2.THRESH_BINARY, at_bs, -0.3, binarizationMethod=cv2.ximgproc.BINARIZATION_NICK)
monoImg = cv2.adaptiveThreshold(monoImg, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
monoImg = cv2.bitwise_not(monoImg)

angle = correct_skew(monoImg)
print(angle)
if not -0.1 < angle < 0.1:
	monoImg = rotate_image(monoImg, angle)
	myImage = rotate_image(myImage, angle)

erosion_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 1))
erosion = cv2.erode(monoImg, erosion_kernel, iterations = 1)
dilation_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (18, 5))
dilation = cv2.dilate(erosion, dilation_kernel, iterations = 1)
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

dilation_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 1))
dilation = cv2.dilate(monoImg, dilation_kernel, iterations = 1)


newImage = numpy.full(myImage.shape, 255, dtype=numpy.uint8)
for cnt in contours:
	x, y, w, h = cv2.boundingRect(cnt)
	
	ROI = dilation[y:y+h, x:x+w]
	# 1 1 0 1 1   # original 1 right pad
	# 1 1 1 0 1   # original 2 right pad
	ROI1 = numpy.c_[ROI, numpy.zeros(len(ROI),dtype=numpy.uint8)]
	#   1 1 0 1 1 # right shift 1
	#   1 1 1 0 1 # right shift 2
	ROI2 = numpy.c_[numpy.zeros(len(ROI),dtype=numpy.uint8), ROI]
	# 0 1 0 0 1 0 # AND 1
	# 0 1 1 0 0 0 # AND 2
	gap = cv2.bitwise_and(ROI1, ROI2) # gap between pixel
	#0 1 1 0 1 0  # reduced
	reducedArray = cv2.reduce(gap, 0, cv2.REDUCE_MAX, dtype=cv2.CV_8U)
	rcnt, length, i, left, started = [], len(reducedArray[0]), 0, 0, False
	while i < length:
		if reducedArray[0][i] >0:
			if not started:
				left = i-1
				started = True
		else:
			if started:
				rcnt.append((x+left, y, i-left, h)) # absolute coordination
				started = False
		i=i+1
	for rc in rcnt:
		_x, _y, _w, _h = rc
		if _h//_w>=2 or _w//_h>=3:
			continue
		xhigh = _x + _w//2 + _h//2
		yhigh = _y + _h//2 + _w//2
		xlow = xhigh - _h
		ylow = yhigh - _w
		if xlow <0:
			xlow = 0
			xhigh = _h
		if xhigh > my_w:
			xhigh = my_w
			xlow = xhigh - _h
		if ylow <0:
			ylow = 0
			yhigh = _w
		if yhigh > my_h:
			yhigh = my_h
			ylow = yhigh - _w
		if xlow >=0 and xhigh<=my_w and ylow >=0 and yhigh<=my_h:
			glyph = numpy.ascontiguousarray(numpy.rot90(myImage[_y:_y+_h, _x:_x+_w], -1))
			cv2.rectangle(myImage, (_x, _y), (_x+_w, _y+_h), (255, 255, 255), -1)
			newImage[ylow:yhigh, xlow:xhigh] = cv2.min(newImage[ylow:yhigh, xlow:xhigh], glyph)
cv2.imwrite(sys.argv[1]+".rotate.png", cv2.min(newImage,myImage))

[willus]

Originally Posted by schezuk

I wrote myself a dirty piece of code to rotate every CJK character by 90 degrees, which is designed to ignore Latin script if a word is wide enough. Not an effective implementation but with many magic numbers, though...

A little context here would be useful. What is your script intended to be used on? A PDF file? Does it work on any PDF?

[willus]

Originally Posted by Used1

Is this where I can ask for help with getting the formatting down? A lot of this is way over my head. And can I post screenshots here to show what I mean?

I can try to help. Have you looked at my help page or watched the "Getting started" videos (which are, admittedly, a bit dated)?

[schezuk]

Originally Posted by schezuk

As some people may not be accustomed to TTB-RTL text in mainland China, or LTR-TTB text in other regions, it may be convient to Text Reflow from one way to another. E.g.

[snip over-large image]

By text reflowing it does not only mean to arrange text elements, which may be easily done by many other softwares, but to re-arrange text in scanned images.

It may be solved like this. Just rotate every square-shaped character detected 90 degree clockwise/counter-clockwise, and leave other characters be.

A workaround in similar principle for text element has been figured out by using a dedicated font with Chinese characters rotated. See LTR to TBRTL.

Originally Posted by willus

I'll put this on my feature request list, but it will be low priority because (1) it's not a common request, and (2) the implementation, even if I didn't have to try and tell western alphabets from asian characters, would be fraught with error. Determining exactly where the gaps are in the characters and which to rotate and which not to rotate would be mistake prone and would no doubt frustrate the end user. Sorry.

The code above works on extracted images rather than pdf files.
It binarizes the image, rotates it by 90 degrees counter-clockwise, aligns it horizontally, and filters out noise pixels.
Next steps are:
Reverse the color.
Every white pixel will color adjacent pixels to make sure every radical of a single, square character is connected to one another.
Chop the result to disconnected pieces.
Rotate 90 degrees clockwise every rectangular piece (but in the original image) that is square enough.

My solution works on single characters. It would be better to process text by line.

[Used1]

Originally Posted by willus

I can try to help. Have you looked at my help page or watched the "Getting started" videos (which are, admittedly, a bit dated)?

I did look over the text, yes, and I'm not sure if it applies to my issue. But I could be missing something because this gets overwhelming fast. So, sorry if it's a stupid question. I really love that you made this, it's made reading papers so much easier when I can get it to work lol.

So the attached image is where I'm at currently and what the problem is. It's always something about maybe the headers/footers getting jumbled, or footnotes? I tried the -1 col thing but that didn't work. Is it the margins?

[willus]

Originally Posted by Used1

So the attached image is where I'm at currently and what the problem is. It's always something about maybe the headers/footers getting jumbled, or footnotes? I tried the -1 col thing but that didn't work. Is it the margins?

It's very hard to tell what is happening without the source PDF document. Can you post that document, or maybe a couple of pages of it? Or send to me privately? It looks like it has some extra text that is screwing up the re-flow (the "GE Public..." text rotated 90 degrees in your preview image).

[Used1]

Originally Posted by willus

It's very hard to tell what is happening without the source PDF document. Can you post that document, or maybe a couple of pages of it? Or send to me privately? It looks like it has some extra text that is screwing up the re-flow (the "GE Public..." text rotated 90 degrees in your preview image).

Attached the PDF, it's an academic paper. That's what I use this program for and it sometimes gets jumbled if they're in columns too. I appreciate your help!


[Removed academic paper that was marked with a recent Copyright notice]

[willus]

Originally Posted by Used1

Attached the PDF, it's an academic paper. That's what I use this program for and it sometimes gets jumbled if they're in columns too. I appreciate your help!

This is why it really helps to see the source file. It has text along the bottom left edges, and that's causing problems. You have to crop out that area of the page. Fortunately you have a nice clean document.

Conversion #1
See k2pdfopt_window.png and cropregion_window.png attachments.
Here I tried to stay as close to your options as possible
1. I selected a crop region to ignore the edges of the page.
2. I selected "Erase horizontal lines" to ignore the divider lines on some of
the pages.
3. I de-selected auto-crop. That's really meant to be for poorly scanned
books that have photocopying artifacts at the edges of the pages. It doesn't
work very well.
4. I set max columns to 1 just so k2pdfopt would not look for multiple columns
5. I put -fc- in the additional options box to suppress a warning message.

It's also important that "Autostraighten" is selected. I set the font size to
18 points. I attached sample output to output_sample_18ptfont.pdf. The conversion was also pretty slow--200 s on my PC. See below for how I remedied that.

Conversion #2
(k2pdfopt_window_faster.png and
output_sample_faster.pdf)
Here I made some changes to make the conversion faster and the output file smaller. Rather than setting the output font size, which is slow for a number of reasons (part of it that k2pdfopt has to analyze the file to determine the font size), I just tell k2pdfopt that your kindle window size is much smaller than it actually is. It seems strange, but this has the same effect, and k2pdfopt processes everything at a lower dpi, so it goes much faster and makes a smaller output file. Also, I set the input dpi to 300 dpi since that is the native resolution of the source file. Normally it defaults to twice the output dpi. Finally, I added "-rt 0" so k2pdfopt does not waste time verifying the orientation of the source file. This brought the conversion time down from 200 s to 45 s on my PC. The output file size is ~11 MB.

Conversion #3
(k2pdfopt_window_fastest_and_smallest.png and output_sample_fastest_and_smallest.pdf)
I added -bpc 1 to the additional options since the source document is just a monochrome (1-bit) document. This gets the output down to ~7 MB from an original size of ~20 MB in conversion #1.