This is an experiment to identify duplicate 78 record labels from the always impressive Internet Archive using the approach suggested in this blog post. I found it difficult to get workable OCR for many of the labels so I applied a few image preprocessing steps and then merged the HOCR results. The code explains this in some detail, but as per the blog post, the initial images were collected:
ia search "collection:georgeblood" --itemlist | head -100 | parallel -j4 'ia download {} --no-directories --format="Item Image"'
In this case, the images were collected in a folder called test100. The first python script is used to apply the image preprocessing using the OCRD Olena utility and the OCR with Tesseract OCR:
python labelProc.py -f test100
There are 4 passes carried out for the OCR by default, and the results are captured in corresponding HOCR files:
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage.jpg
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_sauvola_ms.jpg
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_singh.jpg
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_wolf.jpg
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage.hocr
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_sauvola_ms.hocr
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_singh.hocr
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_wolf.hocr
You can skip the OCR step and do this separately (using parallel for example: find test100 -name '*.jpg' | parallel -j4 tesseract {} {.} hocr) by using the s switch:
python labelProc.py -f test100 -s
The script is for convenience, all of the above can be done with batch tools if desired. The HOCR option in Tesseract is used in order to get the probability numbers for OCR accuracy. The second python script combines the HOCR results into one document based on the probability numbers and creates a single combined text verion:
python mergeHocr.py -f test100
The combined results are in two files with a "_odw" suffix for each image in the folder (if the image has HOCR, sometimes no OCR is possible):
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_odw.hocr
test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_odw.txt
The idea is that the resulting HOCR file is the best of the, in this case, 4 passes of the OCR. I found this was one way to get a usable level of OCR for similarity processing with my test set but there may be better ways of doing this. Finally, the last script uses the method described here:
python identSim.py -f test100 -d doc/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_odw.txt
[nltk_data] Downloading package punkt to /home/ledsys/nltk_data...
[nltk_data] Package punkt is already up-to-date!
collect sentence tokens...
separate into words...
build dictionary...
now corpus...
create TFID...
create similarity index...
prep query_doc...
search for similarity...
reverse sort scores...
show results...
sim match: test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-luth_gbia0333184b_itemimage_odw.txt
score: 1.0000001
sim match: test100/78_1-a-handful-of-earth-from-my-dear-mothers-grave-2-the-cruiskeen-lawn_frank-lu_gbia0412575b_itemimage_odw.txt
score: 0.43742537
sim match: test100/78_1-a-bushel-and-a-peck-2-my-time-of-day_vivian-blaine-and-the-hot-box-girls-rob_gbia0035785d_itemimage_odw.txt
score: 0.1598242
sim match: test100/78_1-a-basketful-of-nuts_gbia8000354d_itemimage_odw.txt
score: 0.12936018
sim match: test100/78_1-a-wise-bird_laura-littlefield-loomis-johnstone-hollis-dann_gbia0201218a_itemimage_odw.txt
score: 0.11242671
Notice that the document for matching is distinct from the folder holding the OCR files. In this case, the document literally has a copy in the folder, hence the perfect (1.000) match. By default, the top 5 document matches are shown. The index and associated parts are built on the first invocation, but are saved and loaded from disk if run multiple times. I suspect there could be refinements in the text of the OCR to improve the matching, for example, removing branding text, e.g. Columbia, but this is meant to be more of a starting point than a definitive example.