2015/09/20 by Rakesh Achanta, Trevor Hastie, Achanta, Rakesh +1 · 1 citation
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Image Processing and 3D Reconstruction #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.CV #cs.LG #cs.NE #stat.ML
paper · pdf · doi:10.48550/arxiv.1509.05962
openalex publication_date 2015/09/20 · arxiv created 2017/02/15 · arxiv updated 2017/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.