BI-LSTM Based Encoding and GAN for Text-to-Image Synthesis

Synthesizing images from text is to produce images with reliable content as specified text depiction that is an extremely demanding task with the most important problems like: content consistency and visual realism. Owing to considerable progression of GAN, it is now possible to produce images with good visual certainty. The translation of text descriptions to images with higher content reliability, on the other hand, is still a work in progress. This paper intends to frame a novel text-to-image synthesis approach, which includes two major phases namely; (1) Text to image encoding and (2) GAN. Initially, during text to image encoding, cross modal feature alignment takes place including text and image features. Consequently, BI-LSTM is deployed to transfer the text embedding to feature vector. At second stage, the image is synthesized based on the encoding. Consequently, text feature group are given as input to GAN, which offers the final synthesized images. Finally, the supremacy of developed approach is examined via evaluation over extant techniques.