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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Papers
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23 Sep 2007
TL;DR: Different preprocessing combined with different feature sets are presented and the dependencies of the feature sets from preprocessing steps are discussed and their performances are compared using the IFN/ENIT-database of handwritten Arabic words.
Abstract: Preprocessing and feature extraction are very important steps in automatic cursive handwritten word recognition. Based on an offline recognition system for Arabic handwritten words which uses a semi-continuous 1-dimensional Hidden Markov Model recognizer, different preprocessing combined with different feature sets are presented. The dependencies of the feature sets from preprocessing steps are discussed and their performances are compared using the IFN/ENIT-database of handwritten Arabic words. As the lower and upper baseline of each word are part of the ground truth of the database, the dependency of the feature set from the accuracy of the estimated baseline is evaluated.
53 citations
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01 Oct 2016TL;DR: The experimental results showed that the proposed framework could achieve much better performance than the state-of-the-art methods and can also be applied to other time sequence problems, such as speech recognition and video analysis.
Abstract: It is well known that the handwritten Chinese text recognition is a difficult problem since there are a large number of classes. In order to solve this problem, we proposed a whole new framework for unconstrained handwritten Chinese text recognition. The core module of the framework is the heterogeneous CNN trained by deep knowledge. The experimental results showed that our proposed method could achieve much better performance than the state-of-the-art methods (96.28% vs. 91.39% of CR on CASIA test set). Moreover, since the proposed framework is general, it can also be applied to other time sequence problems, such as speech recognition and video analysis.
53 citations
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18 Mar 2011TL;DR: An attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network, which yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition.
Abstract: Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30×20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition.
53 citations
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24 Aug 2014TL;DR: The Handwritten Online Musical Symbols (HOMUS) dataset is presented, which consists of 15200 samples of 32 types of musical symbols from 100 different musicians and can establish a binding point in the field of recognition of online handwritten music notation and serve as a baseline for future developments.
Abstract: A profitable way of digitizing a new musical composition is by using a pen-based (online) system, in which the score is created with the sole effort of the composition itself. However, the development of such systems is still largely unexplored. Some studies have been carried out but the use of particular little datasets has led to avoid objective comparisons between different approaches. To solve this situation, this work presents the Handwritten Online Musical Symbols (HOMUS) dataset, which consists of 15200 samples of 32 types of musical symbols from 100 different musicians. Several alternatives of recognition for the two modalities -online, using the strokes drawn by the pen, and offline, using the image generated after drawing the symbol- are also presented. Some experiments are included aimed to draw main conclusions about the recognition of these data. It is expected that this work can establish a binding point in the field of recognition of online handwritten music notation and serve as a baseline for future developments.
53 citations
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TL;DR: An automatic container-code recognition system is developed by using computer vision to segment characters for various imaging conditions and the efficiency and effectiveness of the proposed technique for practical usage are demonstrated.
Abstract: Highlights? An automatic container-code recognition system is developed by using computer vision. ? The characteristics of characters are made full use of to locate container-code. ? A two-step method is proposed to segment characters for various imaging conditions. Automatic container-code recognition is of great importance to the modern container management system. Similar techniques have been proposed for vehicle license plate recognition in past decades. Compared with license plate recognition, automatic container-code recognition faces more challenges due to the severity of nonuniform illumination and invalidation of color information. In this paper, a computer vision based container-code recognition technique is proposed. The system consists of three function modules, namely location, isolation, and character recognition. In location module, we propose a text-line region location algorithm, which takes into account the characteristics of single character as well as the spatial relationship between successive characters. This module locates the text-line regions by using a horizontal high-pass filter and scanline analysis. To resolve nonuniform illumination, a two-step procedure is applied to segment container-code characters, and a projection process is adopted to isolate characters in the isolation module. In character recognition module, the character recognition is achieved by classifying the extracted features, which represent the character image, with trained support vector machines (SVMs). The experimental results demonstrate the efficiency and effectiveness of the proposed technique for practical usage.
53 citations