Package 'image.textlinedetector'

Text Line Segmentation based on the A* Path Planning Algorithm

Description

Text Line Segmentation based on the A* Path Planning Algorithm

Usage

image_textlines_astar(x, morph = FALSE, step = 2, mfactor = 5, trace = FALSE)

Arguments

x	an object of class magick-image
morph	logical indicating to apply a morphological 5x5 filter
step	step size of A-star
mfactor	multiplication factor in the cost heuristic of the A-star algorithm
trace	logical indicating to show the evolution of the line detection

Value

a list with elements

• n: the number of lines found

• overview: an opencv-image of the detected areas

• paths: a list of data.frame's with the x/y location of the baseline paths

• textlines: a list of opencv-image's, one for each rectangular text line area

• lines: a data.frame with the x/y positions of the detected lines

Examples

library(opencv)
library(magick)
library(image.textlinedetector)
path   <- system.file(package = "image.textlinedetector", "extdata", "example.png")
img    <- image_read(path)
img    <- image_resize(img, "x1000")
areas  <- image_textlines_astar(img, morph = TRUE, step = 2, mfactor = 5, trace = TRUE)
areas  <- lines(areas, img)
areas$n
areas$overview
areas$lines
areas$textlines[[2]]
areas$textlines[[4]]
combined <- lapply(areas$textlines, FUN=function(x) image_read(ocv_bitmap(x)))
combined <- do.call(c, combined)
combined
image_append(combined, stack = TRUE)



plt <- image_draw(img)
lapply(areas$paths, FUN=function(line){
  lines(x = line$x, y = line$y, col = "red")  
})
dev.off()
plt

---

Crop an image to extract only the region containing text

Description

Applies a sequence of image operations to obtain a region which contains relevant texts by cropping white space on the borders of the image. This is done in the following steps: morphological opening, morphological closing, blurring, canny edge detection, convex hull contours of the edges, keep only contours above the mean contour area, find approximated contour lines of the convex hull contours of these, dilation and thresholding.

Usage

image_textlines_crop(x)

Arguments

x	an object of class magick-image

Value

an object of class magick-image

Examples

library(opencv)
library(magick)
library(image.textlinedetector)
path  <- system.file(package = "image.textlinedetector", "extdata", "example.png")
img   <- image_read(path)
image_info(img)
img   <- image_textlines_crop(img)
image_info(img)

---

Text Line Segmentation based on valley finding in projection profiles

Description

Text Line Segmentation based on valley finding in projection profiles

Usage

image_textlines_flor(
  x,
  light = TRUE,
  type = c("none", "niblack", "sauvola", "wolf")
)

Arguments

x	an object of class magick-image
light	logical indicating to remove light effects due to scanning
type	which type of binarisation to perform before doing line segmentation

Value

a list with elements

• n: the number of lines found

• overview: an opencv-image of the detected areas

• textlines: a list of opencv-image's, one for each text line area

Examples

library(opencv)
library(magick)
library(image.textlinedetector)
path   <- system.file(package = "image.textlinedetector", "extdata", "example.png")
img    <- image_read(path)
img    <- image_resize(img, "1000x")
areas  <- image_textlines_flor(img, light = TRUE, type = "sauvola")
areas  <- lines(areas, img)
areas$n
areas$overview
combined <- lapply(areas$textlines, FUN=function(x) image_read(ocv_bitmap(x)))
combined <- do.call(c, combined)
combined
image_append(combined, stack = TRUE)

---

Find Words by Connected Components Labelling

Description

Filter the image using the gaussian kernel and extract components which are connected which are to be considered as words.

Usage

image_wordsegmentation(x, kernelSize = 11L, sigma = 11L, theta = 7L)

Arguments

x	an object of class opencv-image containing black/white binary data (type CV_8U1)
kernelSize	size of the kernel
sigma	sigma of the kernel
theta	theta of the kernel

Value

a list with elements

• n: the number of lines found

• overview: an opencv-image of the detected areas

• words: a list of opencv-image's, one for each word area

Examples

library(opencv)
library(magick)
library(image.textlinedetector)
path  <- system.file(package = "image.textlinedetector", "extdata", "example.png")
img   <- image_read(path)
img   <- image_resize(img, "x1000")
areas <- image_textlines_flor(img, light = TRUE, type = "sauvola")
areas$overview
areas$textlines[[6]]
textwords <- image_wordsegmentation(areas$textlines[[6]])
textwords$n
textwords$overview
textwords$words[[2]]
textwords$words[[3]]

---

Extract the polygons of the textlines

Description

Extract the polygons of the textlines as a cropped rectangular image containing the image content of the line segmented polygon

Usage

## S3 method for class 'textlines'
lines(x, image, crop = TRUE, channels = c("bgr", "gray"), ...)

Arguments

x	an object of class textlines as returned by image_textlines_astar or image_textlines_flor
image	an object of class magick-image
crop	extract only the bounding box of the polygon of the text lines
channels	either 'bgr' or 'gray' to work on the colored data or on binary greyscale data
...	further arguments passed on

Value

the object x where element textlines is replaced with the extracted polygons of text lines

Examples

## See the examples in ?image_textlines_astar or ?image_textlines_flor

---

Deslant images by putting cursive text upright

Description

This algorithm sets handwritten text in images upright by removing cursive writing style. One can use it as a preprocessing step for handwritten text recognition.

• image_deslant expects a magick-image and performs grayscaling before doing deslanting

• ocv_deslant expects a ocv-image and does not perform grayscaling before doing deslanting

Usage

ocv_deslant(image, bgcolor = 255, lower_bound = -1, upper_bound = 1)

image_deslant(image, bgcolor = 255, lower_bound = -1, upper_bound = 1)

Arguments

image	an object of class opencv-image (for ocv_deslant) with pixel values between 0 and 255 or a magick-image (for image_deslant)
bgcolor	integer value with the background color to use to fill the gaps of the sheared image that is returned. Defaults to white: 255
lower_bound	lower bound of shear values. Defaults to -1
upper_bound	upper bound of shear values. Defaults to 1

Value

an object of class opencv-image or magick-image with the deslanted image

Examples

library(magick)
library(opencv)
library(image.textlinedetector)
path <- system.file(package = "image.textlinedetector", "extdata", "cursive.png")
img  <- ocv_read(path)
img  <- ocv_grayscale(img)
img
up   <- ocv_deslant(img)
up

img  <- image_read(path)
img
image_deslant(img)

---