63 lines
1.8 KiB
R
63 lines
1.8 KiB
R
# A script to OCR/parse/mangle Collections label-images
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# Note - this takes ~2 seconds per label-image
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# (c) 2019 The Field Museum - MIT License (https://opensource.org/licenses/MIT)
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# https://github.com/fieldmuseum/Collections-OCR
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library(tidyr)
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library(magick)
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library(stringr)
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library(tesseract)
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# download relevant languages/training data
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tesseract_download("lat") # Latin
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tesseract_download("deu") # German
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# get list of JPG & JPEG image files
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imagelist <- list.files(path = "images/", pattern = ".jp|.JP")
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# setup table for OCRed text
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imagesOCR <- data.frame("image" = rep("", NROW(imagelist)),
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"line_count" = rep("", NROW(imagelist)),
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"text" = rep("", NROW(imagelist)),
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stringsAsFactors = F)
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imagesOCR$line_count <- as.integer(imagesOCR$line_count)
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# loop through each label-image
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for (i in 1:NROW(imagelist)) {
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# OCR the image to text
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ocrText <- image_read(paste0("images/", imagelist[i])) %>%
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image_ocr(language = c("eng", "lat", "deu"))
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imagesOCR$text[i] <- ocrText
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# include filename & count of lines in row
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imagesOCR$image[i] <- imagelist[i]
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imagesOCR$line_count[i] <- str_count(ocrText, "\n+")
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# show progress
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print(paste(i, " - ", Sys.time()))
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}
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# split text lines to separate columns
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ocrText <- separate(imagesOCR, text,
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into = paste0("Line",
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seq(1:max(imagesOCR$line_count, na.rm = T))),
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# into = seq(1:20), # if need consistent NCOL
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sep = "(\n)+",
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extra = "merge", fill = "right")
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# export CSV
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write.csv(ocrText,
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paste0("ocrText-",
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gsub("\\s+|:", "", Sys.time()),
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".csv"),
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na = "",
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row.names = F) |