The image.textlinedetector R package detects text lines in digital images and segments these into words.
Objective of the package is to more easily plug the text lines in Handwritten Text Recognition modelling frameworks like the one explained in this document
The algorithms in this R package implement the following techniques:
- An Implementation of a Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents paper link
- A Statistical approach to line segmentation in handwritten documents paper link
- A new normalization technique for cursive handwritten words paper link
More descriptions of technique 2 can be found in this document
- For regular users, install the package from your local CRAN mirror
install.packages("image.textlinedetector")
- For installing the development version of this package and to execute the example
install.packages("opencv")
install.packages("magick")
install.packages("image.binarization")
remotes::install_github("DIGI-VUB/image.textlinedetector")
Look to the documentation of the functions
help(package = "image.textlinedetector")
Based on the paper An Implementation of a Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents
library(opencv)
library(magick)
library(image.binarization)
library(image.textlinedetector)
#path <- "C:/Users/Jan/Desktop/OCR-HTR/RABrugge_TBO119_693_088.jpg"
path <- system.file(package = "image.textlinedetector", "extdata", "example.png")
img <- image_read(path)
img <- image_binarization(img, type = "su")
areas <- image_textlines_astar(img, morph = TRUE, step = 2, mfactor = 5)
areas <- lines(areas, img, channels = "bgr")
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
library(opencv)
library(magick)
library(image.binarization)
library(image.textlinedetector)
path <- system.file(package = "image.textlinedetector", "extdata", "example.png")
img <- image_read(path)
img
img_bw <- image_binarization(img, type = "isauvola")
areas <- image_textlines_flor(img, light = TRUE, type = "sauvola")
areas$overview
areas$textlines[[6]]
areas <- lines(areas, img_bw, channels = "gray")
textwords <- image_wordsegmentation(areas$textlines[[10]])
textwords$n
textwords$overview
textwords$words[[2]]
textwords$words[[3]]
combined <- lapply(textwords$words, FUN = function(x) image_read(ocv_bitmap(x)))
combined <- do.call(c, combined)
combined
By DIGI: Brussels Platform for Digital Humanities: https://digi.research.vub.be