Although there are some reports on offline Tamil isolated handwritten character recognition, to our knowledge there is only two reports on Tamil off-line handwritten word recognition. Also no city name dataset is avai...
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ISBN:
(纸本)9781479901937
Although there are some reports on offline Tamil isolated handwritten character recognition, to our knowledge there is only two reports on Tamil off-line handwritten word recognition. Also no city name dataset is available for Tamil script. In this paper we present a Tamil offline city name dataset, we developed, and propose a scheme for recognition. Because of the different writing style of various individuals, some of the characters in a Tamil city name may touch and accurate segmentation of such touching into individual characters is a difficult task. Avoiding proper segmentation here, we consider a city name string as a word and the recognition problem is treated as lexicon driven word recognition. In the proposed method, binarized city names are pre-segmented into primitives (individual character or its parts). Primitive components of each city name are then merged into possible characters to get the best city name using dynamic programming. For merging, total likelihood of characters is used as the objective function and character likelihood is computed based on Modified Quadratic Discriminant Function (MQDF), where direction features are applied. A dataset of 265 Tamil city names is developed. and the database will be available freely to the researchers. From the experiment of the proposed scheme 96.89% city name accuracy is obtained from this dataset.
The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarpi...
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Keyword spotting in video document images is challenging due to low resolution and complex background of video images. We propose the combination of Texture-Spatial-Features (TSF) for keyword spotting in video images ...
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ISBN:
(纸本)9781479961016
Keyword spotting in video document images is challenging due to low resolution and complex background of video images. We propose the combination of Texture-Spatial-Features (TSF) for keyword spotting in video images without recognizing them. First, a segmentation method extracts words from text lines in each video image. Then we propose the set of texture features for identifying text candidates in the word image with the help of k-means clustering. The proposed method finds proximity between text candidates to study the spatial arrangement of pixels that result in feature vectors for spotting words in the input frame. The proposed method is evaluated on word images of different fonts, contrasts, backgrounds and font sizes, which are chosen from standard databases such as ICDAR 2013 video and our video data. Experimental results show that the proposed method outperforms the existing method in terms of recall, precision and f-measure.
Use of social media for communication, sharing expressing views, broadcasting news, threatening and blackmailing has become an integral part of society. One such activity is understanding multi-cultural wedding images...
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This paper presents a novel model for understanding social image content through text localization. For text localization, we explore Maximally Stable Extremal Regions (MSER) for detecting components, that works by cl...
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This paper presents the results of the Handwriting Segmentation Contest that was organized in the context of the ICDAR2013. The general objective of the contest was to use well established evaluation practices and pro...
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ISBN:
(纸本)9781479901937
This paper presents the results of the Handwriting Segmentation Contest that was organized in the context of the ICDAR2013. The general objective of the contest was to use well established evaluation practices and procedures to record recent advances in off-line handwriting segmentation. Two benchmarking datasets, one for text line and one for word segmentation, were created in order to test and compare all submitted algorithms as well as some state-of-the-art methods for handwritten document image segmentation in realistic circumstances. Handwritten document images were produced by many writers in two Latin based languages (English and Greek) and in one Indian language (Bangla, the second most popular language in India). These images were manually annotated in order to produce the ground truth which corresponds to the correct text line and word segmentation results. The datasets of previously organized contests (ICDAR2007, ICDAR2009 and ICFHR2010 Handwriting Segmentation Contests) along with a dataset of Bangla document images were used as training dataset. Eleven methods are submitted in this competition. A brief description of the submitted algorithms, the evaluation criteria and the segmentation results obtained from the submitted methods are also provided in this manuscript.
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and pu...
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Recently, video text detection, tracking and recognition in natural scenes are becoming very popular in the computervision community. However, most existing algorithms and benchmarks focus on common text cases (e.g.,...
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Since OCR engines are usually script-dependent, automatic text recognition in multi-script document requires a pre-processor module that identifies the scripts. Based on this motivation, in this paper, we present a wo...
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Since OCR engines are usually script-dependent, automatic text recognition in multi-script document requires a pre-processor module that identifies the scripts. Based on this motivation, in this paper, we present a word level handwritten Indian script identification technique. To handle this, words are first segmented by morphological dilation and performed connected component labelling. We then employ the Radon transform, discrete wavelet transform, statistical filters and discrete cosine transform to extract the directional multi-resolution spatial features. We tested the features by using linear discriminant analysis, support vector machine and K-nearest neighbour classifiers over 11 different major Indian scripts (including Roman) in bi-script and tri-script scenario. In our tests, we have achieved maximum accuracies of 98% and 96% for bi-script and tri-scipt respectively.
This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this co...
This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using .a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed. Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.
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