A irregular text detection via dilated recombination and efficient reorganization on natural scene

In recent years, scene text detection has brought out broader prospects via growing applied opportunities. Nevertheless, pointing out which detected capability and suitable instantaneity in equilibrium is an essential consideration of irregular text detection. Out of consideration for the trouble, we propose an efficient scene text detector that unites a Dilated Recombined Unit (DRU) and a Efficient Reorganized Unit (ERU), named DENet. In the beginning, input feature information is received into a DR-VanillaNet backbone. Dilated recombined unit is devised to insert into every block of DR-VanillaNet to heighten the connection about distant pixel points. Next, an FPN with efficient reorganized unit tends to exploit feature redundancy and permutate channels partially. Correspondingly, DRU and ERU work on constructive effect for precision with a limited descent of speed. Moreover, a progressive scale expansion is carried forward which maintains the ability to generate the adjacent instances successfully. Multiple experiments on CTW1500, Total-Text benchmark datasets prove that designed model intends to improve precision accompanied by a limited drop of speed. It is specifically indicated that the value of precision on these two datasets reaches 84.29% and 85.30%. And FPS are achieved by 8.6 and 10.9, respectively.