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.
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01 Jan 2003
TL;DR: The long history of research in this area, commercial success, and the continuing need and ability to handle less restricted forms of text make OCR the most important application area in machine perception to date.
Abstract: Optical character recognition (OCR) is performed by optical character readers which are automated electronic systems. OCR may be defined as the process of converting images of machine printed or handwritten numerals, letters, and symbols into a computer- processable format. The long history of research in this area, commercial success, and the continuing need and ability to handle less restricted forms of text make OCR the most important application area in machine perception to date.
30 citations
16 Dec 2012
TL;DR: This work has recognized offline handwritten Gurmukhi characters with different combinations of features and classifiers with a recognition accuracy of 94.8% when PCA is not applied and a Recognition accuracy of 97.7% whenPCA is applied.
Abstract: Offline handwritten character recognition (OHCR) is the method of converting handwritten text into machine processable layout Since late sixties, efforts have been made for offline handwritten character recognition throughout the world Principal Component Analysis (PCA) has also been used for extracting representative features for character recognition In order to assess the prominence of features in offline handwritten Gurmukhi character recognition, we have recognized offline handwritten Gurmukhi characters with different combinations of features and classifiers The recognition system first sets up a skeleton of the character so that significant feature information about the character can be extracted For the purpose of classification, we have used k-NN, Linear-SVM, Polynomial-SVM and RBF-SVM based approaches In present work, we have collected 7,000 samples of isolated offline handwritten Gurmukhi characters from 200 different writers The set of basic 35 akhars of Gurmukhi has been considered here A partitioning policy for selecting the training and testing patterns has also been experimented in present work We have used zoning feature; diagonal feature; directional feature; intersection and open end points feature; transition feature; parabola curve fitting based feature and power curve fitting based feature extraction technique in order to find the feature set for a given character The proposed system achieves a recognition accuracy of 948% when PCA is not applied and a recognition accuracy of 977% when PCA is applied
30 citations
23 Mar 2011
TL;DR: This paper proposes a system for recognition of offline handwritten Malayalam vowels using Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification.
Abstract: Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification.
30 citations
TL;DR: A comprehensive review of ACR techniques and evaluate the status of the ACR system development and an up to date bibliography is presented.
Abstract: An optical character recognition (OCR) system may provide a solution to the data entry problems, a bottleneck for the data processing industry. Therefore, OCR systems are being developed for almost all major languages and Arabic language is no exception to it. During the past three decades, considerable research and development works have been done towards the development of an efficient Arabic optical character recognition (ACR) system. In this paper we present a comprehensive review of ACR techniques and evaluate the status of the ACR system development and an up to date bibliography.
30 citations
01 Sep 2001
TL;DR: The construction of a model for off-line word recognizers based on over-segmentation of the input image and recognition of segment combinations as characters in a given lexicon word is described.
Abstract: We describe the construction of a model for off-line word recognizers based on over-segmentation of the input image and recognition of segment combinations as characters in a given lexicon word. One such recognizer, the Word Model Recognizer (WMR), is used extensively. Based on the proposed model it was possible to improve the performance of WMR.
30 citations