SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 3: Dataset statistics.
ohsumed	reuter	20news	amazon	classic
Number of documents	7497	7585	18776	7854	6778
Number of training documents	3268	5413	11265	5497	4744
Number of test documents	4229	2172	7511	2357	2034
Size of the vocabulary	12144	13761	28825	21816	12904
Unique words in a document	94.5	63.0	137.1	201.8	60.8
Number of classes	10	8	20	4	4
Split type	one-fold	one-fold	one-fold	five-fold	five-fold
Table 3 summarizes the statistics of the datasets after preprocessing. Ohsumed (Joachims, 1998) consists of medical abstracts. Reuter (Sebastiani, 2002) and 20news (Lang, 1995) are news datasets. Amazon (Blitzer et al., 2007) consists of reviews in amazon.com. Classic (SMART) consists of academic papers. The datasets are retrieved from https://github.com/mkusner/wmd.
B Usage of LKH
We used LKH version 3.0.6, with parameter PATCHING_C = 3, PATCHING_A = 2, which are the default parameters. As the LKH solver accepts only integral values, we multiply the actual distance by  $10^{3}$  and round down the values before we feed them into the LKH solver. The difference caused by this rounding process is negligibly small.
The number  $k$  of neighbors in the kNN classification is selected from  $\{1,2,\dots ,19\}$ . The variance of the Gaussian filter in a blurred bag of words is selected from  $\{0.01,0.1,\dots ,1000\}$ . We selected the hyperparameters using a 5-fold cross-validation and retrained the kNN model using the chosen hyperparameters and entire training dataset.