109 lines
4.1 KiB
Markdown
109 lines
4.1 KiB
Markdown
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# OCR challenge for index cards
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The goal of this task is to post-process the output from the Tesseract
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OCR engine. Alternatively, it could be treated as an OCR, as images
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are also available.
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The data set is based on the index cards from [Korpus Frazeologiczny
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Języka Polskiego](http://leksykografia.amu.edu.pl/).
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## Metrics
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The task will be evaluated using the following metrics:
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* CharMatch (main metric) — it measures the deviation between the
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output, the input (as obtained from Tesseract OCR) and the input;
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see
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<https://re-research.pl/ltc-2017-iayko-jassem-gralinski-obrebski.pdf>,
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page 4 (CharMatch penalizes unwanted changes more than WER/CER).
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* WER (Word Error Rate) — the equivalent of CER for words (number of words inserted, substituted
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and deleted divided by the total number of words).
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* CER (Character Error Rate) — the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance)
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between real text and the OCR engine output, divided by the total number of chacracters,
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## Evaluation
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You can carry out evaluation using the [GEval](https://gitlab.com/filipg/geval),
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when you generate `out.tsv` files (in the same format as `expected.tsv` files):
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```
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wget https://gonito.net/get/bin/geval
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chmod u+x geval
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./geval -t dev-0
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```
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## Directory structure
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* `README.md` — this file
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* `config.txt` — GEval configuration file
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* `images/` — images to be processed, referenced in TSV files
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* `in-header.tsv` — one-line TSV file with column names for input data
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* `out-header.tsv` — one-line TSV file with column names for the output data
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* `train/` — directory with hand-annotated gold-standard OCR train data
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* `train/in.tsv` — input data for the train set
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* `train/expected.tsv` — expected (reference) data for the dev set
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* `dev-0/` — directory with hand-annotated gold-standard OCR test data
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* `dev-0/in.tsv` — input data for the dev set
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* `dev-0/expected.tsv` — expected (reference) data for the dev set
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* `test-A/` — directory with hand-annotated gold-standard OCR test data
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* `test-A/in.tsv` — input data for the test set
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* `test-A/expected.tsv` — expected (reference) data for the test set (hidden)
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Note that we mean TSV, *not* CSV files. In particular, double quotes
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are not considered special characters here! In particular, set
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`quoting` to `QUOTE_NONE` in the Python `csv` module:
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```python
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import csv
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with open('file.tsv', 'r') as tsvfile:
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reader = csv.reader(tsvfile, delimiter='\t', quoting=csv.QUOTE_NONE)
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for item in reader:
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pass
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```
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### Downloading image files
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Image files are kept using [git-annex](https://git-annex.branchable.com).
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If you need them, install git-annex and run `./annex-get-all.sh`.
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## Format of the test sets
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The input file (`in.tsv`) consists of 4 TAB-separated columns:
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* the name of the test image (MD5 digest of the binary content with `.png` extension); these
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files are in `images/` directory,
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* the ISO-639-3 language code of the source document (always `pol`),
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* the pixel depth of an image (always 400),
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* the output from Tesseract OCR to be corrected.
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(The 2nd and 3rd field is for compatibility with other OCR challenges,
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it's always, respectively, `pol` and `400` in this challenge.)
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Each entry in the `expected.tsv` contains the text recognized from the test image.
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The carriage returns (CR) and backslashs are replaced with `\n` and
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`\\` respectively, so you should decode them, using for example this
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Python code
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def decode_text(t):
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return t.replace('\\n', '\n').replace('\\\\', '\\')
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Each line in the `expected.tsv.xz` corresponds to the line in
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`in.tsv.xz`.
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All the files are UTF-8 encoded.
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Note that `out.tsv` and `expected.tsv` files have the `.tsv` extension
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only for consistency. Actually they are just plain text files.
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### Submission format
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Each entry in `expected.tsv` contains entire text file to be
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recognized, compressed to one line. In order to achieve best possible
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results, one should format submitted `out.tsv` in similar way, i.e.
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don't forget to encode backslashes and carriage returns:
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def encode_text(t):
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return t.replace('\\', '\\\\').replace('\n', '\\n')
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