Characters In Natural Scenes Research Articles

Text localization and recognition of Chinese characters in natural scenes based on improved faster R-CNN

Text detection and recognition are widely used in daily life. Although it is a very rich market, it has very difficult challenges in practical application. The complex and changeable natural scenes lead to the complex background of the text in the image, which also reflects the research value of text information recognition and extraction in natural scenes. To solve the many problems faced by the recognition of Chinese characters, such as complex shapes and diverse structures, this paper uses VGG16 to extract features and introduces a two-layer bidirectional LSTM network. It improves Faster R-CNN by using a RPN to extract candidate boxes and adjust the position of candidate regions. In this paper, the improved model Faster BLSTM-CNN is tested, and the effectiveness experiment of feature extraction, the difference comparison before and after the improvement of the algorithm, and the comparison experiment with the traditional recognition algorithm are carried out respectively. And it finally carried out an experimental comparison of the combination of text recognition and positioning, and obtained the results. The algorithm Faster BLSTM-CNN in this paper is better in the localization and recognition of Chinese characters in the dataset. In the natural scene, the recognition rate of Faster BLSTM-CNN in this paper is 81.54%, the positioning accuracy is 88.14%, and the detection speed is 86 ms, which has improved performance. Therefore, the improvement of Faster R-CNN is effective. It can effectively locate and recognize Chinese characters in natural scenes.

Radical analysis network for learning hierarchies of Chinese characters

Chinese characters have a valuable property, this is, numerous Chinese characters are composed of a compact set of fundamental and structural radicals. This paper introduces a radical analysis network (RAN) that makes full use of this valuable property to implement radical-based Chinese character recognition. The proposed RAN employs an attention mechanism to extract radicals from Chinese characters and to detect spatial structures among the radicals. Then, the decoder in RAN generates a hierarchical composition of Chinese characters based on the knowledge of the extracted radicals and their internal structures. The method of treating a Chinese character as a composition of radicals rather than as a single character category is a human-like method that can reduce the size of the vocabulary, ignore redundant information among similar characters and enable the system to recognize unseen Chinese character categories, i.e., zero-shot learning. Through experiments, we assess the practicality of RAN for recognizing Chinese characters in natural scenes. Furthermore, a RAN framework can be proposed for scene text recognition with the extension of a dense recurrent neural network (denseRNN) encoder, a multihead coverage attention model and HSV representations. The proposed approach achieved the best performance in the ICPR MTWI 2018 competition.

Dataset of Urduud1k from Natural Scenes

In latest years research has drawn attention on text analysis in natural scenes. Databases play a significant part in the efficiency assessment of the algorithm for text recognition. A data set of natural scene text images in six distinct languages have recently been released in an International Conference on Document Analysis and Recognition (ICDAR).This dataset is for multi-languages except Urdu. In the natural images of the Urdu scene, there is an absence of a conventional Urdu text database. This research therefore mainly aims to build a database for Urdu text in natural scenes. The dataset is very large because there are 10 distinct cameras with distinct resolution, distinct angles and distinct range requirements for each picture captured by distinct light zone. The dataset comprises of Urdu words, ligatures and characters in natural scenes. The dataset contains 16k images of words, 32k ligatures and characters images. This dataset contains 1kimagesincluding signboard, a name of the store, banners and so on. In addition, the Urdu dataset is contrasted with the current data set including ICRAR 2003, ARASTI, Chars 74k, etc. The dataset includes many images from the natural scene so it can be used in natural environments to identify Urdu text.