Abstract
We present a balanced and 100% machine annotated Bangla OCR corpus of nearly eight and a half million characters. This is a “first-of-its-kind” effort for the Bangla language. Although Bangla is a top five most frequently used language in the world, it is still considered to be a resource-poor language since even the most basic language processing tools are not available. This corpus goes a long way in mitigating this shortcoming. Also, it is important to mention that this is a continuation of our previous work in building a synthetic corpus for training Bangla OCRs and is an intermediate step in our effort to build a true gold-standard corpus for Bangla OCRs—our ultimate goal. The paper not only discusses the corpus, but also the entire processing pipeline—from scanning pages to identifying lines to segmenting words and finally characters—which are then annotated using a homegrown OCR. We also present a comprehensive corpus characteristics and specification review—demonstrating that we have built a corpus which is very nearly balanced—a very desirable characteristic of any corpus design.
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Rifat, M.J.R., Banik, M., Hasan, N., Nahar, J., Rahman, F. (2021). A Novel Machine Annotated Balanced Bangla OCR Corpus. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_13
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