Topic
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.
Papers published on a yearly basis
Papers
More filters
18 Sep 2012
TL;DR: A novel segmentation-based and lexicon-driven handwritten English recognition systems using convolutional neural networks for offline character recognition and modified online segmentation method based on rules are presented.
Abstract: This paper presents a novel segmentation-based and lexicon-driven handwritten English recognition systems. For the segmentation, a modified online segmentation method based on rules are applied. Then, convolutional neural networks are introduced for offline character recognition. Experiments are evaluated on UNIPEN lowercase data sets, with the word recognition rate of 92.20%.
31 citations
01 Dec 2008
TL;DR: A novel segmentation based approach is proposed for recognition of offline handwritten Devanagari words and a hidden Markov model is used for recognition at pseudocharacter level.
Abstract: A novel segmentation based approach is proposed for recognition of offline handwritten Devanagari words. Stroke based features are used as feature vectors. A hidden Markov model is used for recognition at pseudocharacter level. The word level recognition is done on the basis of a string edit distance.
31 citations
19 Dec 2014
TL;DR: A novel technique to recognize handwritten Bangla word is proposed using Histograms of Oriented Gradients to represent each word sample at the feature space and a neural network based classifier is applied to classify the word images.
Abstract: The holistic approaches for handwritten word recognition treat the words as single, indivisible entity and attempt to recognize words from their overall shape. In the present work, a novel technique to recognize handwritten Bangla word is proposed. Histograms of Oriented Gradients (HOG) are used as the feature set to represent each word sample at the feature space and a neural network based classifier is applied to classify the word images. On the basis of the HOG feature set, the performance achieved by the technique on a small dataset is quite satisfactory.
31 citations
25 Aug 2013
TL;DR: A distance function based on Levenshtein metric to compute the similarity between an unknown character sample and each training sample is formulated and the effect of pruning the training sample set based on the above distance between individual training samples of the same character class is studied.
Abstract: In this article, we propose a novel scheme for online handwritten character recognition based on Levenshtein distance metric. Both shape and position information are considered in our feature representation scheme. The shape information is encoded by a string of quantized values of angular displacements between successive sample points along the trajectory of the handwritten character. The consecutive occurrences of same value in such a string are removed retaining only one of them. Next, each element in the resulting string is assigned an integral weight value proportional to the length of the segment of the trajectory represented by the corresponding element. Similarly, position information is encoded by another string of quantized positional information along with their respective weight values. We formulated a distance function based on Levenshtein metric to compute the similarity between an unknown character sample and each training sample. Here, we have also studied the effect of pruning the training sample set based on the above distance between individual training samples of the same character class. The proposed approach has been simulated on different publicly available sample databases of online handwritten characters. The recognition accuracies are acceptable.
31 citations
06 Aug 2002
TL;DR: This paper investigates various confidence measures and their integration in an isolated word recognition system as well as in a sentence recognition system.
Abstract: In this paper we study the use of confidence measures for an on-line handwriting recognizer. We investigate various confidence measures and their integration in an isolated word recognition system as well as in a sentence recognition system. In isolated word recognition tasks, the rejection mechanism is designed in order to reject the outputs of the recognizer that are possibly wrong, which is the case for badly written words, out-of-vocabulary words or general drawing. In sentence recognition tasks, the rejection mechanism allows rejecting parts of the decoded sentence.
31 citations