# Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor
###### Abstract  
Reading text and unified text detection and recognition from natural images are the most challenging applications in computer vision and document analysis. Previously proposed end-to-end scene text reading methods do not consider the frequency of input images at feature extraction, which slows down the system, requires more memory, and recognizes text inaccurately. In this paper, we proposed an octave convolution (OctConv) feature extractor and a time-restricted attention encoder-decoder module for end-to-end scene text reading. The OctConv can extract features by factorizing the input image based on their frequency. It is a direct replacement of convolutions, orthogonal and complementary, for reducing redundancies and helps to boost the reading text through low memory requirements at a faster speed. In the text reading process, features are first extracted from the input image using Feature Pyramid Network (FPN) with OctConv Residual Network with depth 50 (ResNet50). Then, a Region Proposal Network (RPN) is applied to predict the location of the text area by using extracted features. Finally, a time-restricted attention encoder-decoder module is applied after the Region of Interest (RoI) pooling is performed. A bilingual real and synthetic scene text dataset is prepared for training and testing the proposed model. Additionally, well-known datasets including ICDAR2013, ICDAR2015, and Total Text are used for fine-tuning and evaluating its performance with previously proposed state-of-the-art methods. The proposed model shows promising results on both regular and irregular or curved text detection and reading tasks. octave convolution; bilingual scene text reading; Ethiopic script; attention +
Footnote †: journal:  
problem being used as a text-reading task. To improve these, an end-to-end scene text spotting method was presented in references [8; 9; 10], but it still needs improvement in terms of recognition accuracy, memory usage, and speed. For instance, in [11; 12] a fully conventional network is applied for scene text detection and recognition by considering the detection and recognition problems independently. For scene text detection, a convolutional neural network (CNN) was applied to extract feature maps from the input image, and then different decoders were used to decode and detect the text region based on the extracted features [5; 13; 14]. Using the extracted sequences of features at the scene text detection phase, characters/words have been predicted with sequence prediction models [15; 16]. These types of approaches led to heavy time cost and ignored the correlation in visual cues for images with a number of text regions, whereas both operations had real integrations. In general, previously proposed scene text detection and recognition approaches were problematic, especially when texts in the image are written in more than one script, different text sizes and text shapes are irregular. Furthermore, most research focused on English language and only a few presented other languages such as Arabic and Chinese. Except our previously presented scene text recognition method [17], there is no research output for scene text reading as well as scene text detection for Ethiopia script-based languages. Ethiopic script is used as a writing system for more than 43 languages, including Amharic, Geez, and Tigrigna. Amharic is the official language of Ethiopia and the second-largest Semitic language after Arabic [18]. On the other hand, English is used as a teaching medium in secondary schools and higher education. As a result, English and Amharic languages are being used concurrently for different activities in most areas of the country. Thus, designing independent applications of scene text detection and scene text recognition requires multiple networks for solving individual sub-problems, which increases computational complexity and causes accuracy and integrity problems.