Abstract
The performance various academic and commercial text recognition solutions for many languages world-wide has been satisfactory. Many projects now use the ocr as a reliable module. As of now, Indian languages are far away from this state, which is unfortunate. Beyond many challenges due to script and language, this space is adversely affected by the scattered nature of research, lack of systematic evaluation, and poor resource dissemination. In this work, we aim to design and implement a web-based system that could indirectly address some of these aspects that hinder the development of ocr for Indian languages. We hope that such an attempt will help in (i) providing and establishing a consolidated view of state-of-the-art performances for character and word recognition at one place (ii) sharing resources and practices (iii) establishing standard benchmarks that clearly explain the capabilities and limitations of the recognition methods (iv) bringing research attempts from a wide variety of languages, scripts, and modalities into a common forum. We believe the proposed system will play a critical role in further promoting the research in the Indian language text recognition domain.
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Overfitting is a modeling error that occurs when a function is too closely fit a limited set of data points.
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Tulsyan, K., Srivastava, N., Mondal, A., Jawahar, C.V. (2020). A Benchmark System for Indian Language Text Recognition. In: Bai, X., Karatzas, D., Lopresti, D. (eds) Document Analysis Systems. DAS 2020. Lecture Notes in Computer Science(), vol 12116. Springer, Cham. https://doi.org/10.1007/978-3-030-57058-3_6
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