**PP-OCR** is an OCR system that consists of three parts, text detection, detected boxes rectification and text recognition. The purpose of text detection is to locate the text area in the image. In PP-OCR, Differentiable Binarization (DB) is used as text detector which is based on a simple segmentation network. It integrates feature extraction and sequence modeling. It adopts the Connectionist Temporal Classification (CTC) loss to avoid the inconsistency between prediction and label.

**Policy Similarity Metric**, or **PSM**, is a similarity metric for measuring behavioral similarity between states in reinforcement learning. It assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. PSM is reward-agnostic, making it more robust for generalization compared to approaches that rely on reward information.

Policy Similarity Metric

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

PP-OCR

PP-OCR: A Practical Ultra Lightweight OCR System

A **ShuffleNet Block** is an image model block that utilises a [channel shuffle](https://paperswithcode.com/method/channel-shuffle) operation, along with depthwise convolutions, for an efficient architectural design. It was proposed as part of the [ShuffleNet](https://paperswithcode.com/method/shufflenet) architecture. The starting point is the [Residual Block](https://paperswithcode.com/method/residual-block) unit from [ResNets](https://paperswithcode.com/method/resnet), which is then modified with a pointwise group [convolution](https://paperswithcode.com/method/convolution) and a channel shuffle operation.

Source	PP-OCR: A Practical Ultra Lightweight OCR System
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com