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Handwritten bangla compound character recognition using gradient feature

Handwritten bangla compound character recognition using grad...

引用

10th International Conference on Information Technology, ICIT 2007

作者： Pal, U. Wakabayashi, T. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Graduate School of Engineering Mie University 1577 Kurimamachiya-cho Tsu Mie Japan

ISBN: (纸本)0769530680

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 X 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. © 2007 IEEE.

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A system for off-line oriya handwritten character recognition using curvature feature

A system for off-line oriya handwritten character recognitio...

引用

10th International Conference on Information Technology, ICIT 2007

ISBN: (纸本)0769530680

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 4949 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 π/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 77 blocks from 4949 blocks and a directional resolution is made to get 8 directions from 32 directions. Using curvature features for 3 levels we get 1176 (77 blocks × 8 directions × 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. © 2007 IEEE.

关键词： Principal component analysis

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A Two Stage recognition Scheme for Handwritten Tamil Characters

A Two Stage Recognition Scheme for Handwritten Tamil Charact...

引用

International Conference on Document Analysis and recognition

作者： U. Bhattacharya S.K. Ghosh S. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

India is a multilingual multiscript country with more than 18 languages and 10 different major scripts. Not enough research work towards recognition of handwritten characters of these indian scripts has been done. Tamil, an official as well as popular script of the southern part of India, Singapore, Malaysia, and Sri Lanka has a large character set which includes many compound characters. Only a few works towards handwriting recognition of this large character set has been reported in the literature. Recently, HP Labs India developed a database of handwritten Tamil characters. In the present paper, we describe an off-line recognition approach based on this database. The proposed method consists of two stages. In the first stage, we apply an unsupervised clustering method to create a smaller number of groups of handwritten Tamil character classes. In the second stage, we consider a supervised classification technique in each of these smaller groups for final recognition. The features considered in the two stages are different. The proposed two-stage recognition scheme provided acceptable classification accuracies on both the training and test sets of the present database.

关键词： Character recognition Handwriting recognition Spatial databases pattern recognition Testing Speech recognition computer vision Natural languages Clustering methods Personal communication networks

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Direction Code Based Features for recognition of Online Handwritten Characters of Bangla

Direction Code Based Features for Recognition of Online Hand...

引用

International Conference on Document Analysis and recognition

作者： U. Bhattacharya B.K. Gupta S. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In the present article, we describe a novel direction code based feature extraction approach for recognition of online Bangla handwritten basic characters. We have implemented the proposed approach on a database of 7043 online handwritten Bangla (a major script of the indian subcontinent) character samples, which has been developed by us. This is a 50-class recognition problem and we achieved 93.90% and 83.61% recognition accuracies respectively on its training and test sets.

关键词： Character recognition Handwriting recognition Spatial databases pattern recognition Histograms Shape Testing Personal digital assistants computer vision Feature extraction

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SVM Based Scheme for Thai and English Script Identification

SVM Based Scheme for Thai and English Script Identification

引用

International Conference on Document Analysis and recognition

作者： S. Chanda O.R. Terrades U. Pal Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India Computer Vision Center Universitat Authnòma de Barcelona Barcelona Spain

In some Thai documents, a single text line of a document page may contain both Thai and English scripts. For the optical character recognition (OCR) of such a document page it is better to identify, at first, Thai and English script portions and then to use individual OCR system of the respective scripts on these identified portions. In this paper, a SVM based method is proposed for identification of word-wise printed English and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of the individual character group combining different character features obtained from structural shape, profile, component overlapping information, topological properties, water reservoir concept etc. Based on the experiment on 6110 data we obtained 99.36% script identification accuracy from the proposed scheme.

关键词： Support vector machines Optical character recognition software Support vector machine classification computer vision Optical network units Structural shapes Water resources Reservoirs Neural networks pattern recognition

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Handwritten Numeral recognition of Six Popular indian Scripts

Handwritten Numeral Recognition of Six Popular Indian Script...

引用

International Conference on Document Analysis and recognition

作者： U. Pal N. Sharma T. Wakabayashi F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Tsu Japan

ISBN: (纸本)9780769528229;0769528228

India is a multi-lingual multi-script country but there is not much work towards handwritten character recognition of indian languages. In this paper we propose a modified quadratic classifier based scheme towards the recognition of off-line handwritten numerals of six popular indian scripts. Here we consider Devnagari, Bangla, Telugu, Oriya, Kannada and Tamil scripts for our experiment. The features used in the classifier are obtained from the directional information of the numerals. For feature computation, the bounding box of a numeral is segmented into blocks and the directional features are computed in each of the blocks. These blocks are then down sampled by a Gaussian filter and the features obtained from the down sampled blocks are fed to a modified quadratic classifier for recognition. Here we have used two sets of feature. We have used 64 dimensional features for high-speed recognition and 400 dimensional features for high-accuracy recognition in our proposed system. A five-fold cross validation technique has been used for result computation and we obtained 99.56%, 98.99%, 99.37%, 98.40%, 98.71% and 98.51% accuracy from Devnagari, Bangla, Telugu, Oriya, Kannada, and Tamil scripts, respectively.

关键词： Handwriting recognition Natural languages Character recognition Automation pattern recognition Filters Sorting computer vision Feature extraction Writing

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Off-Line Handwritten Character recognition of Devnagari Script

Off-Line Handwritten Character Recognition of Devnagari Scri...

引用

International Conference on Document Analysis and recognition

作者： U. Pal N. Sharma T. Wakabayashi F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Tsu Japan

In this paper we present a system towards the recognition of off-line handwritten characters of Devnagari, the most popular script in India. 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 2times2 mean filtering is applied 4 times on the gray level image and a non-linear size normalization is done on the image. The normalized image is then segmented to 49times49 blocks and a Roberts filter is applied 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 blocks and the directions are down sampled using Gaussian filter to get 392 dimensional feature vector. A modified quadratic classifier is applied on these features for recognition. We used 36172 handwritten data for testing our system and obtained 94.24% accuracy using 5-fold cross-validation scheme.

关键词： Character recognition Handwriting recognition Filters Filtering pattern recognition Image segmentation Automation computer vision System testing Natural languages

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Handwritten Bangla Compound Character recognition Using Gradient Feature

Handwritten Bangla Compound Character Recognition Using Grad...

引用

International Conference on Information Technology (ICIT)

作者： U. Pal T. Wakabayashi F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Tsu Mie Japan

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.

关键词： Character recognition Handwriting recognition Filters pattern recognition Filtering Automation Information technology computer vision Writing Frequency

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A System for Off-Line Oriya Handwritten Character recognition Using Curvature Feature

A System for Off-Line Oriya Handwritten Character Recognitio...

引用

International Conference on Information Technology (ICIT)

作者： U. Pal T. Wakabayashi F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Tsu Mie Japan

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.

关键词： Character recognition Shape Handwriting recognition computer vision Spatial resolution Speech recognition Information technology pattern recognition Image segmentation Interpolation

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Text/Graphics Separation in Color Maps

Text/Graphics Separation in Color Maps

引用

International Conference on Computing: Theory and Applications, ICCTA

作者： Partha Pratim Roy Josep Llados Umapada Pal Computer Science Department Universitat Autònoma de Barcelona Barcelona Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India

Text/graphics separation in document image analysis is one of the main concerns in present research work. The complexity enhances when both text and graphics overlap in the context of maps in color images. This paper discusses a number of improvements to text/graphics separation methods to make it suitable for maps. Emphasize is given to the overlapping regions of text and graphics. It also discusses a method of color separation using clustering method for the purpose of text/graphics separation

关键词： computer graphics Image color analysis Image segmentation Histograms Bit error rate computer vision Text analysis Clustering methods Engineering drawings Testing

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