You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Deep Learning Models for Retinal Blood Vessels Segmentation: A Review 
Chapter Title:  3) POOLING LAYER 
1. Abstract of Cited Paper (BIB001):  Full end-to-end text recognition in natural images is a challenging problem that has received much attention recently. Traditional systems in this area have relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this paper, we take a different route and combine the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a common framework to train highly-accurate text detector and character recognizer modules. Then, using only simple off-the-shelf methods, we integrate these two modules into a full end-to-end, lexicon-driven, scene text recognition system that achieves state-of-the-art performance on standard benchmarks, namely Street View Text and ICDAR 2003.

Literature Review:
The purpose of the pooling layer is to ensure the shift-invariance and lowers the computational burden by reducing the resolution of the feature maps. It is usually placed after the convolutional layer. It takes the feature map which is generated from the convolutional layer and outputs a single value for each receptive field (pooling window) according to the pooling operation. The pooling layer performs max pooling BIB001 , sum, and mean pooling BIB002 . Figure 9 shows different pooling operations. Also, there are other versions of pooling layers proposed for some tasks, such as Spatial Pyramid Pooling (SPP) BIB002 that can generates a fixed length of features regardless of the input size.