Burmese Text Images Classification of Based on Neural Network

Abstract

The first stage of Burmese and Russia's bilingual scene text detection and recognition is the detection of Burmese and Russia's scene text. The detection results are mainly divided into three categories: successfully detected regions of Burmese, Russian text, and non-words regions with failed predictions. If the detected text image results are accurately classified, then the non- text images should be filtered in the recognition phase, meanwhile, the Burmese and Russia text images can be identified by using different classifiers, such a procedure can reduce the complexity of classification and recognition of two different characters by one recognition model. Accurate classification of Burmese and Russian text images is mattered. This paper researches the classification of Burmese and Russian text image and non-text images by using convolutional neural networks. We perform a series of exploration about the classification accuracy of Burmese and Russian text images and non-text images and compare the accuracy with the classification results based on the transfer learning then analyze it. The results show that using a 7-layer convolutional neural network has reached saturation, and increasing the network depth does not improve the results, which provides reference values for Burmese and Russia text image classification.