Text-based visual question answering with knowledge base

Text-based Visual Question Answering(VQA) usually needs to analyze and understand the text in a picture to give a correct answer for the given question. In this paper, a generic Text-based VQA with Knowledge Base (KB) is proposed, which performs text-based search on text information obtained by optical character recognition (OCR) in images, constructs task-oriented knowledge information and integrates it into the existing models. Due to the complexity of the image scene, the accuracy of OCR is not very high, and there are often cases where the words have individual character that is incorrect, resulting in inaccurate text information; here, some correct words can be found with help of KB, and the correct image text information can be added. Moreover, the knowledge information constructed with KB can better explain the image information, allowing the model to fully understand the image and find the appropriate text answer. The experimental results on the TextVQA dataset show that our method improves the accuracy, and the maximum increment is 39.2%.