Online handwriting recognition research has recently received significant thrust. Specifically for Indian scripts, handwriting recognition has not been focused much till in the near past. However, due to generous Gove...
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Online handwriting recognition research has recently received significant thrust. Specifically for Indian scripts, handwriting recognition has not been focused much till in the near past. However, due to generous Government funding through the group on Technology Development for Indian Languages (TDIL) of the Ministry of Communication & Information Technology (MC&IT), Govt. of India, research in this area has received due attention and several groups are now engaged in research and development works for online handwriting recognition in different Indian scripts. An extensive bottleneck of the desired progress in this area is the difficulty of collection of large sample databases of online handwriting in various scripts. Towards the same, recently a user-friendly tool on Android platform has been developed to collect data on handheld devices. This tool is called ISIgraphy and has been uploaded in the Google Play for free download. This application is designed well enough to store handwritten data samples in large scales in user-given file names for distinct users. Its use is script independent, meaning that it can collect and store handwriting samples written in any language, not necessarily an Indian script. It has an additional module for retrieval and display of stored data. Moreover, it can directly send the collected data to others via electronic mail.
Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text...
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In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied...
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ISBN:
(纸本)9781479918065
In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied in the context of word spotting and this scarcity of research work is the motivation of the paper. This paper presents a comparative study of classical Dynamic Time Warping (DTW) technique and many of its improved modifications, as well as other sequence matching techniques in the context of word spotting. An experimental study on historical documents is performed to evaluate the behavior of DTW's variants and other sequence matching techniques. A detailed comparative analysis along with wide range of experimentation is performed, which shows that classical DTW remains a good choice when there are no segmentation problems and when features are very local. In case of word segmentation errors, Continuous Dynamic Programming (CDP) seems to be a better choice. This research work has introduced several other improved sequence matching algorithms in the context of word spotting, which show interesting and improved results.
This paper addresses the problem of creating a handwritten character recognizer, which makes use of both labelled and unlabelled data to learn continuously over time to make the recognisor adaptable. The proposed meth...
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This paper addresses the problem of creating a handwritten character recognizer, which makes use of both labelled and unlabelled data to learn continuously over time to make the recognisor adaptable. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for better parameter estimation, especially when labelled data is scarce and expensive unlike unlabelled data. We introduce an algorithm for learning from labelled and unlabelled samples based on the combination of novel online ensemble of the Randomized Naive Bayes classifiers and a novel incremental variant of the Expectation Maximization (EM) algorithm. We make use of a weighting factor to modulate the contribution of unlabelled data. An empirical evaluation of the proposed method on Tamil handwritten base character recognition proves efficacy of the proposed method to carry out incremental semi-supervised learning and producing accuracy comparable to state-of-the-art batch learning method. Online handwritten Tamil characters from the IWFHR 2006 competition dataset was used for evaluating the proposed method.
This work addresses the problem of creating a Bayesian Network based online semi-supervised handwritten character recognisor, which learns continuously over time to make a adaptable recognisor. The proposed method mak...
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ISBN:
(纸本)9781479952106
This work addresses the problem of creating a Bayesian Network based online semi-supervised handwritten character recognisor, which learns continuously over time to make a adaptable recognisor. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for boosting the accuracy, especially when labelled data is scarce and expensive unlike unlabelled data. An algorithm is introduced to perform semi-supervised learning based on the combination of novel online ensemble of the Randomized Bayesian network classifiers and a novel online variant of the Expectation Maximization (EM) algorithm. We make use of a novel varying weighting factor to modulate the contribution of unlabelled data. Proposed method was evaluated using online handwritten Tamil characters from the IWFHR 2006 competition dataset. The accuracy obtained was comparable to the state of the art batch learning methods like HMM and SVMs.
In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier. An estimate of the uniformity in stroke thickness is one of our fe...
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In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier. An estimate of the uniformity in stroke thickness is one of our features and we obtain the same using only a subset of the distance transform values of the concerned region. Estimation of the uniformity in stroke thickness on the basis of sparse sampling of the distance transform values is a novel approach. Another feature is the distance between the foreground and background colors computed in a perceptually uniform and illumination-invariant color space. Remaining features include two ratios of anti-parallel edge gradient orientations, a regularity measure between the skeletal representation and Canny edgemap of the object, average edge gradient magnitude, variation in the foreground gray levels and five others. Here, we present the results of the proposed approach on the ICDAR 2003 database and another database of scene images consisting of text of Indian scripts.
recognition of handwritten characters of Indian script is difficult because of the presence of many complex shaped compound characters (cluster characters) as well as variability involved in the writing style of diffe...
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recognition of handwritten characters of Indian script is difficult because of the presence of many complex shaped compound characters (cluster characters) as well as variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten compound characters using modified quadratic discriminant function (MQDF). The features used for recognition purpose are mainly based on directional information obtained from the arc tangent of the gradient. To get the feature, at first, a 2 times 2 mean filtering is applied 4 times on the gray level image and a non-linear size normalization is done on the image. A Roberts filter is then applied on the normalized image to obtain gradient image. Next, the arc tangent of the gradient (direction of gradient) is initially quantized into 32 directions and the strength of the gradient is accumulated with each of the quantized direction. Finally, the frequencies of these directions are down sampled using Gaussian filter to get 392 dimensional feature vectors. Using 5-fold cross validation technique we obtained 85.90% accuracy from a dataset of Bangla compound characters containing 20,543 samples.
recognition of handwritten similar shaped character is a difficult problem and in character recognition system most of the errors occur from similar shaped characters. In this paper we proposed a novel feature extract...
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ISBN:
(纸本)9781424445004
recognition of handwritten similar shaped character is a difficult problem and in character recognition system most of the errors occur from similar shaped characters. In this paper we proposed a novel feature extraction technique to improve the recognition results of two similar shaped characters. The technique is based on F-ratio (Fisher Ratio), a statistical measure defined by the ratio to the between-class variance and within-class variance. F-ratio modifies the feature vector of two similar shape characters by weighting the feature elements. This weighting scheme enhances the feature elements that belongs to the distinguishable portions of the similar shaped characters and reduces the feature elements of the common portion of the characters, so that similar shaped characters can be identified easily. We considered pair of handwritten similar shape characters of different scripts like Arabic/Persian, Devnagari English, Bangla, Oriya, Tamil, Kannada, Telugu etc. and we noted that f-ratio based feature weighting shows better recognition results.
In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To g...
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In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To get the feature, at first, the input image is size normalized and segmented into 49times49 blocks. Curvature is then computed using bi-quadratic interpolation method and quantized into 3 levels according to concave, linear and convex regions. Next direction of gradient is quantized into 32 levels with pi/16 intervals, and strength of the gradient is accumulated in each of the 32 directions and in each of the 3 curvature levels of every block. A spatial resolution is made to get 7times7 blocks from 49times49 blocks and a directional resolution is made to get 8 directions from 32 directions. Using curvature features for 3 levels we get 1176 (7times7 blocks times 8 directions times 3 levels) dimensional features. Finally using principal component analysis we reduce the dimension 1176 to 392 and this 392 dimensional feature vector is fed to a quadratic classifier for recognition. We tested 18190 samples of Oriya handwritten samples and obtained 94.60% accuracy from our proposed system.
A simple, easy to implement and low cost mobile health(m-health) solution using general packet radio service (GPRS) of globally extended cellular mobile network for economically deprived countries is described in this...
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A simple, easy to implement and low cost mobile health(m-health) solution using general packet radio service (GPRS) of globally extended cellular mobile network for economically deprived countries is described in this paper. According to different statistical surveys, heart disease is a major health burden throughout the world but more importantly the third world countries like India, Pakistan, Bangladesh. We consider here a telecardiology system, where the ECG images captured by mobile camera are sent using Multimedia Massage Service (MMS) to a data extraction and processing unit. This unit can extract digitized data from those images, which may be restored as database in a dedicated server and also analyzed by an inbuilt knowledgebase as well as a rough decision generating system. Ultimately, the system will generate an automated report that will suggest the primary treatment and preventive measures to be taken by the patient. The report will be sent back to the patient by Short Message Service (SMS) through the same mobile system. The main advantage of the system is that it will not require constant monitoring or presence of a doctor at the initial stage. The ECG of the patient with critical abnormality or unknown abnormality will be directly sent to the doctorpsilas mobile and intimation will also be sent to the patient site directly. The extreme popularity of mobile phone with camera in the third world countries makes this architecture practically realizable.
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