# Telugu OCR Framework using Deep Learning

arXiv: https://arxiv.org/abs/1509.05962 · HTML: https://mlsanity.com/paper/1509.05962/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [Optimal Projections for Discriminative Dictionary Learning using the JL-lemma](https://mlsanity.com/paper/2308.13991/) (2023)

## Abstract (arXiv)

In this paper, we address the task of Optical Character Recognition(OCR) for the Telugu script. We present an end-to-end framework that segments the text image, classifies the characters and extracts lines using a language model. The segmentation is based on mathematical morphology. The classification module, which is the most challenging task of the three, is a deep convolutional neural network. The language is modelled as a third degree markov chain at the glyph level. Telugu script is a complex alphasyllabary and the language is agglutinative, making the problem hard. In this paper we apply the latest advances in neural networks to achieve state-of-the-art error rates. We also review convolutional neural networks in great detail and expound the statistical justification behind the many tricks needed to make Deep Learning work.