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OCR Synthetic Benchmark Dataset for Indic Languages

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arxiv 2205.02543 v1 pith:MBVEI2TR submitted 2022-05-05 cs.CV

OCR Synthetic Benchmark Dataset for Indic Languages

classification cs.CV
keywords datasyntheticindiclanguagesmodelaccuracyamountbecomes
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present the largest publicly available synthetic OCR benchmark dataset for Indic languages. The collection contains a total of 90k images and their ground truth for 23 Indic languages. OCR model validation in Indic languages require a good amount of diverse data to be processed in order to create a robust and reliable model. Generating such a huge amount of data would be difficult otherwise but with synthetic data, it becomes far easier. It can be of great importance to fields like Computer Vision or Image Processing where once an initial synthetic data is developed, model creation becomes easier. Generating synthetic data comes with the flexibility to adjust its nature and environment as and when required in order to improve the performance of the model. Accuracy for labeled real-time data is sometimes quite expensive while accuracy for synthetic data can be easily achieved with a good score.

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Cited by 1 Pith paper

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  1. GlotOCR Bench: OCR Models Still Struggle Beyond a Handful of Unicode Scripts

    cs.CL 2026-04 unverdicted novelty 7.0

    GlotOCR Bench shows that OCR models perform well on fewer than 10 scripts and fail to generalize beyond about 30, with results tracking pretraining coverage and models hallucinating from known scripts on unfamiliar ones.