Intelligent word recognition

About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.

Odia Offline Typewritten Character Recognition using Template Matching with Unicode Mapping

Abstract: Optical Character Recognition (OCR) is a document image analysis method where scanned digital image that contains either machine printed or handwritten scripts are input into a system to translate it into an editable machine readable digital text format Hence OCR has been a highly challenging topic of interest for researchers all around the globe and also research paper involving OCR that works under all possible conditions and gives highly accurate results It is seen that efficient algorithms have increased the speed and accuracy of character recognition A substantial amount of work has been done on foreign languages such as English, Chinese etc but very few paper are there for Indian languages baring a few for Hindi and Bengali Hence our research work was directed towards development of a novel algorithm for Offline Typewritten Odia Character Recognition using Template Matching with Unicode Mapping

A Simple and Effective Technique for Online Handwritten Bangla Character Recognition

Abstract: In this paper, we have proposed a simple but effective feature extraction technique following the distance-based features to recognize online handwritten isolated Bangla basic characters. In this approach, a character is divided into N number of segments and then distances are calculated among each other. These distance values are then used as features for recognition purpose. On evaluation of this feature set on 10,000 Bangla character samples (50-class character set) by various classifiers, the method yields reasonably good result with 98.20 % success rate.

Similar Handwritten Chinese Character Recognition Based on CNN-SVM

Abstract: For the past several decades, offline handwritten character recognition is widely and deeply studied. The requirements of the identification results are constantly improving in practical applications. However, the recognition rates of the similar handwritten Chinese characters are not very high in different writing style, writing environment and writing mode. We propose a method of combining deep convolution neural network and support vector machine together. Using the deep convolution neural network to learn and extract Chinese characters features automatically, and then the extracted features are classified and identified by the support vector machine. Experiments show that the deep convolution neural network can extract the features effectively, which avoided the shortage of artificial feature extraction, then using the support vector machine to classify and identify that, the accuracy rate is further improved.

Challenges in Segmentation of Text in Handwritten Gurmukhi Script

Abstract: The scanned image of the text is not of any use for user, because that image is not editable. One can not make any change if required to the scanned document. This provides a food for thought for the theory of optical character recognition (OCR). OCR is nothing but character recognition of a segmented part of the scanned image. Therefore the segmented part of the image would be such that it should provide a close relation to the character to be recognised. Hence segmentation plays an important role in the OCR process. There are problems in segmentation process, but the degree of the problems varies from script to script, that is, the problem set for segmentation of the text written in a particular script may differ than the problem set for the text written in other scripts. The characteristics of the script, plays a significant role in deciding the segmentation points. The present study is an effort to find these problems especially for the segmentation of text in Gurmukhi scripts – typed and handwritten.

Off-line handwritten Chinese character recognition with hidden Markov models

Abstract: Off-line handwritten Chinese character recognition is one of the most difficult tasks of optical character recognition because of complexity of patterns, large quantity of classes, many uncertainties, etc. The hidden Markov model (HMM) method has achieved great success in the field of speech recognition. It also exhibits potential advantage in degraded text and handwritten character recognition. We present a modeling and recognition method of off-line handwritten Chinese character with hidden Markov models and its experimental result.