Natural Scene Character Recognition Datasets Datasets description There are four publicly available datasets, i.e. Char74K dataset [1], ICADAR 2003 robust character recognition dataset [2], IIIT5K set [3] and Street View Text (SVT) dataset [4]. We only focus on recognition of English characters, which are composed of 62 classes, i.e. digits 0~9, English letters in upper case A~Z, and lower case a~z. All the images in the experiments are resized into 32*32 pixels and all the images should be first transformed into a gray scale images. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Char74K_15 dataset The character images of the Char74K dataset [1] are mostly photographed from sign boards,hoardings and advertisements and a few images of products are in supermarkets and shops. As for the Char74K dataset, we only use the English character images cropped from the natural scene images. The English dataset has 12503 characters, of which 4798 were labeled as bad images due to excessive occlusion, low resolution or noise. It contains 62 character classes.A small subset with a standard partition is used in our experiments, i.e. Char74K-15, which contains 15 training samples per class and 15 test samples per class. ICDAR2003 Dataset The ICDAR2003 [2] robust reading dataset was collected for the robust reading competition of scene text detection and recognition. The scene character dataset ICDAR03-CH contains more than 11,500 character images. It is a very difficult dataset because of serious non-text background outliers with the cropped character samples, and many character images have very low resolution. There are 6,185 training characters and 5,430 testing characters, respectively. We excluded some special characters such as '!', and then the final experimental dataset consists of 6,113 characters for training and 5,379 characters for testing. IIIT5K Dataset The IIIT 5K-word (IIIT5K) [3] is composed of 2,000 and 3,000 images for testing and training, respectively. The images contain both scene text images and born-digital images. The character image dataset, which is extracted from word images, is composed of 9,678 samples and 15,269 samples for training and testing, respectively. SVT Dataset The Street View Text (SVT) [4] was collected from Google Street View of road-side scenes. All the images are very challenging because of the large variations in illumination, character image sizes, and variety of font sizes and styles. The SVT character dataset, which was annotated in [5],is utilized for evaluating different scene character recognition methods. This dataset consists of 3,796 character samples from 52 categories (no digit images). SVT character dataset is more difficult to recognize than the ICADAR2003 dataset. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Features Extraction Experiments Configuration In the SVT dataset, only the test samples are available for character recognition. Considering that the SVT character dataset has similar distribution to ICDAR2003 and Char74K, we integrated the Char74K EnglishImg dataset and the training samples of ICADAR2003 to construct a new training dataset, which has more than 18600 characters.
Link1: (BaiduLink) Password:ynhs; Link2: (GoogleLink) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [1] Z. Zhang, Y. Xu, C.L. Liu, Natural Scene Character Recognition Using Robust PCA and Sparse Representation, [2] Z. Zhang, Y. Xu, J. Yang, X. Li, D. Zhang, A Survey of Sparse Representation: Algorithms and Applications, IEEE Access,3, 490-530,2015 Copyright: Zheng Zhang, Yong Xu, 2016.06.12 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Copyright Notice References
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