In this paper, we describe a contextual Korean OCR post-processing model considering unknown words. This work starts from the following premises: 1) In the language having very large character set, it is hard to directly correct erroneous string; 2) word formation is deeply related not only to morphological feature but also to phonological feature(esp. syllable combination on the surface level)...

A lot of work has been reported on optical character recognition for various non-Indian scripts like Chinese, English and Japanese and Indian scripts like Tamil, Hindi Telugu, etc. , in this paper, we present a literature review on stemmer, optical character recognition (OCR) and Text mining work on Indian scripts, mainly on the Gujarati languages. We have discussed the different techniques for...

In this paper, we take a pattern recognition approach to correcting errors in text generated from printed documents using optical character recognition (OCR). We apply a very general, theoretically optimal model to the problem of OCR word correction, introduce practical methods for parameter estimation, and evaluate performance on real data.

We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image da...

This short paper analyses an experiment comparing the efficacy of several Named Entity Recognition (NER) tools at extracting entities directly from the output of an optical character recognition (OCR) workflow. The authors present how they first created a set of test data, consisting of raw and corrected OCR output manually annotated with people, locations, and organizations. They then ran each...

The optical character recognition system (OCR) selected by the National Library of Medicine (NLM) as part of its system for automating the production of MEDLINE® records frequently segments the scanned page images into zones which are inappropriate for NLM's application. Software has been created in-house to correct the zones using character coordinate and character attribute information provid...

Optical Character Recognition (OCR) refers to a technology that uses image processing and character recognition algorithms identify characters on an image. This paper is deep study the effect of OCR based Artificial Intelligence (AI) algorithms, in which different AI for analysis are classified reviewed. Firstly, mechanisms characteristics artificial neural network-based summarized. Secondly, t...

With the advent of tablet and touch screen computing, the number of applications that utilize written text recognition technologies is rapidly increasing. These applications rely on fast and accurate optical character recognition (OCR) algorithms. The selforganizing map (SOM) is an unsupervised algorithm capable of great performance in machine learning. As such, it has become a benchmark in OCR...