Models, code, and papers for "S. K. Katti":

Speech Recognition by Machine, A Review

Jan 13, 2010
M. A. Anusuya, S. K. Katti

This paper presents a brief survey on Automatic Speech Recognition and discusses the major themes and advances made in the past 60 years of research, so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in this important area of speech communication. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges (e.g., variations of the context, speakers, and environment).The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. Hence authors hope that this work shall be a contribution in the area of speech recognition. The objective of this review paper is to summarize and compare some of the well known methods used in various stages of speech recognition system and identify research topic and applications which are at the forefront of this exciting and challenging field.

* International Journal of Computer Science and Information Security, IJCSIS, Vol. 6, No. 3, pp. 181-205, December 2009, USA 
* 25 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS December 2009, ISSN 1947 5500, http://sites.google.com/site/ijcsis/ 

  Click for Model/Code and Paper
Chargrid-OCR: End-to-end trainable Optical Character Recognition through Semantic Segmentation and Object Detection

Sep 13, 2019
Christian Reisswig, Anoop R Katti, Marco Spinaci, Johannes Höhne

We present an end-to-end trainable approach for optical character recognition (OCR) on printed documents. It is based on predicting a two-dimensional character grid (\emph{chargrid}) representation of a document image as a semantic segmentation task. To identify individual character instances from the chargrid, we regard characters as objects and use object detection techniques from computer vision. We demonstrate experimentally that our method outperforms previous state-of-the-art approaches in accuracy while being easily parallelizable on GPU (therefore being significantly faster), as well as easier to train.

* 4 pages 

  Click for Model/Code and Paper
BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding

Sep 11, 2019
Timo I. Denk, Christian Reisswig

For understanding generic documents, information like font sizes, column layout, and generally the positioning of words may carry semantic information that is crucial for solving a downstream document intelligence task. Our novel BERTgrid, which is based on Chargrid by Katti et al. (2018), represents a document as a grid of contextualized word piece embedding vectors, thereby making its spatial structure and semantics accessible to the processing neural network. The contextualized embedding vectors are retrieved from a BERT language model. We use BERTgrid in combination with a fully convolutional network on a semantic instance segmentation task for extracting fields from invoices. We demonstrate its performance on tabulated line item and document header field extraction.

* 4 pages 

  Click for Model/Code and Paper