SlowGuess/NAACL_2022_OCR huggingface dataset:
Strategy	Model	bn	hi	gu	pa	mr	te	ta	ml	kn	as	or	ModAvg
Translate-Train	mBERT	47	48	46	45	46	46	47	46	46	43	35	45
IndicBERT	43	44	40	42	41	42	43	39	43	33	41	41
MuRIL	52	54*	52	53	52	51	52	51	54	51	50	52
LnAvg	47	49	46	47	46	47	47	45	48	43	42	46
Translate-Test	mBERT	44	46	43	45	43	45	46	43	44	39	33	43
IndicBERT	36	36	35	34	35	35	35	35	35	36	34	35
MuRIL	53*	52	51	51	51	50	51	51	50	50	49	51
LnAvg	45	44	43	43	43	43	44	43	43	42	39	43
Bilingual-Train	mBERT	49	49	47	47	48	46	49	47	49	46	34	46
IndicBERT	42	41	42	42	40	42	41	44	42	42	41	42
MuRIL	52	53*	52	51	51	52	51	52	51	53	51	52
LnAvg	48	48	47	47	46	47	47	47	47	47	42	47
Multilingual-Train	mBERT	47	47	46	47	46	45	46	45	47	46	36	45
IndicBERT	42	41	42	40	40	42	41	41	40	42	40	41
MuRIL	54*	54*	52	53	52	53	53	53	54*	54*	53	53
LnAvg	47	47	47	47	46	47	47	46	47	47	43	46
EnTranslate-Test	mBERT	51	52	50	51	50	50	52	49	50	50	49	50
IndicBERT	46	48	46	46	46	46	46	45	46	45	44	46
MuRIL	53*	52	51	51	51	50	50	50	51	50	48	51
LnAvg	50	51	49	49	49	49	49	48	49	48	47	49
Translate-Train-X	mBERT	44	44	43	44	43	44	44	43	44	41	34	42
IndicBERT	37	36	36	35	36	37	37	37	36	33	36	36
MuRIL	51	52*	52*	52*	50	51	51	51	52*	51	51	51
LnAvg	44	44	43	44	43	44	44	43	44	42	40	43
Bilingual-TrainX	mBERT	45	44	44	44	44	44	45	44	45	44	37	44
IndicBERT	37	36	36	36	36	37	38	37	37	37	36	37
MuRIL	51	51	51	51	51	51	52*	52*	51	52*	52*	51
LnAvg	44	44	44	44	44	44	45	44	44	44	42	44
Table 7: Performance in terms of accuracy when evaluated on the  $\alpha_{3}$  test set. Higher value implies better performance. Here, LnAvg represents the average accuracy for a language across all models, while ModAvg represents the average accuracy of a model across all languages. A value in Purple represents the best accuracy for that model across all languages. An underlined value in Blue represents the best accuracy for that language across all models. A value in Green with an asterisk(*) represents the cases where language-wise and model-wise values coincide. As we fine-tune on a specific Indic language in the fine-tuning strategies Translate-Train-X and Bilingual-Train-X, we report the training average of the concerned language. We do not include the results of XLM-RoBERTa as the model fails to converge on these experiments on multiple runs with a distinct set of hyper-parameters as explained in Appendix §D.
bn	hi	gu	pa	mr	te	ta	ml	kn	as	or	TrainAvg
bn	67	66	64	62	63	63	63	60	63	64	62	63
hi	66	67	65	65	64	62	63	64	65	62	62	64
gu	63	64	66	65	62	64	63	63	63	63	64	64
pa	63	64	63	65	61	61	62	62	62	62	61	62
mr	65	66	63	64	65	62	62	63	64	62	62	63
te	65	62	63	64	62	64	62	63	64	63	63	63
ta	63	64	63	61	62	62	65	62	64	61	59	62
ml	65	62	62	63	62	63	62	65	64	63	61	63
kn	65	65	65	64	63	63	63	64	66	62	62	64
as	63	63	63	62	63	62	63	63	63	65	61	63
or	64	61	60	62	60	61	61	60	62	62	64	61
TestAvg	64	64	63	63	62	62	63	63	64	63	62	63
bn	hi	gu	pa	mr	te	ta	ml	kn	as	or	TrainAvg
bn	55	54	54	53	53	52	53	53	53	54	52	53
hi	54	56	53	52	52	51	52	52	53	53	52	53
gu	54	52	52	50	51	50	52	50	51	50	50	51
pa	55	54	54	55	53	52	54	52	53	52	53	53
mr	54	53	53	53	53	51	52	51	52	52	52	52
te	54	52	52	52	52	51	51	52	52	52	51	52
ta	56	55	54	51	53	53	54	52	53	51	51	53
ml	53	50	53	49	51	50	50	52	52	51	50	51
kn	54	53	52	51	51	50	52	50	54	51	51	52
as	56	53	55	52	53	52	53	53	54	54	51	53
or	55	51	50	53	51	51	51	51	52	51	53	52
TestAvg	54	53	53	52	52	51	52	52	53	52	51	52
bn	hi	gu	pa	mr	te	ta	ml	kn	as	or	TrainAvg
bn	52	51	50	51	50	49	50	49	51	52	51	50
hi	53	54	51	53	52	51	52	51	52	50	50	52
gu	53	51	52	52	50	51	50	52	51	50	50	51
pa	53	52	51	53	51	51	53	51	51	51	52	52
mr	52	50	50	51	51	48	49	49	49	50	49	50
te	51	51	49	50	50	51	50	50	51	49	49	50
ta	53	52	50	48	51	49	52	51	50	51	50	51
ml	52	49	50	51	52	50	49	50	51	50	49	50
kn	52	53	50	51	50	51	51	51	53	51	50	51
as	51	50	50	51	50	49	50	50	51	51	52	51
or	52	51	49	50	51	50	50	51	51	50	50	50
TestAvg	52	51	50	51	51	50	51	51	51	50	50	51
Table 8: Complete results (accuracy) for Translate-Train-X of MuRIL on the  ${\alpha }_{1},{\alpha }_{2}$  ,and  ${\alpha }_{3}$  test splits respectively.
bn	hi	gu	pa	mr	te	ta	ml	kn	as	or	TrainAvg
bn	67	67	64	65	64	63	63	64	64	63	63	64
hi	65	67	63	65	63	63	63	62	63	63	61	63
gu	65	67	66	65	63	64	64	64	65	64	62	64
pa	66	66	65	66	64	62	64	64	64	64	63	64
mr	64	67	64	65	65	62	64	63	64	64	61	64
te	66	66	65	65	64	66	65	64	65	64	64	65
ta	65	67	65	64	63	63	65	63	64	63	63	64
ml	67	67	66	66	63	63	64	66	63	63	61	64
kn	67	67	65	65	64	63	64	64	66	63	63	65
as	66	66	65	64	65	63	64	64	64	65	62	64
or	65	68	65	65	64	63	65	63	65	63	65	64
TestAvg	66	67	65	65	64	63	64	63	64	63	63	64
bn	hi	gu	pa	mr	te	ta	ml	kn	as	or	TrainAvg
bn	55	54	54	53	53	53	53	52	53	52	52	53
hi	55	54	54	54	53	52	54	53	54	52	52	53
gu	54	54	53	53	53	53	54	53	53	54	52	53
pa	55	55	54	55	53	52	55	54	56	54	53	54
mr	55	54	54	53	53	53	54	53	55	53	52	54
te	55	53	53	53	53	54	53	51	53	53	52	53
ta	53	53	53	53	52	52	53	52	54	52	51	52
ml	57	54	55	53	54	53	53	53	54	53	52	54
kn	56	54	53	54	52	51	53	53	56	54	52	53
as	56	54	54	54	52	53	54	54	55	53	52	54
or	55	54	53	53	52	53	54	54	54	53	53	53
TestAvg	55	54	54	54	53	53	54	53	54	53	52	53
bn	hi	gu	pa	mr	te	ta	ml	kn	as	or	TrainAvg
bn	52	50	49	50	52	51	50	52	50	49	49	50
hi	51	52	49	52	50	49	50	51	51	49	48	50
gu	51	51	51	51	51	51	50	51	51	49	49	51
pa	52	52	50	51	51	51	50	52	50	49	48	51
mr	52	50	51	50	51	50	50	51	50	49	49	50
te	51	51	51	51	52	52	51	51	51	49	50	51
ta	52	52	51	52	51	51	51	51	51	51	50	51
ml	53	52	52	50	52	52	51	52	50	50	50	51
kn	50	52	51	52	50	51	51	50	50	49	48	50
as	53	52	51	52	52	52	51	52	50	53	49	52
or	53	52	51	52	51	50	52	52	51	51	50	51
TestAvg	52	52	51	51	51	51	51	51	50	50	49	51
Table 9: Complete results (accuracy) for Bilingual-Train-X of MuRIL on the  ${\alpha }_{1},{\alpha }_{2}$  ,and  ${\alpha }_{3}$  test splits respectively.