# PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition

arXiv: https://arxiv.org/abs/2109.04145 · HTML: https://mlsanity.com/paper/2109.04145/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 89 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 30 of 60)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://mlsanity.com/paper/2004.02984/) (2020)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://mlsanity.com/paper/2003.12294/) (2020)
- [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://mlsanity.com/paper/1811.00751/) (2018)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [Robust Scene Text Recognition with Automatic Rectification](https://mlsanity.com/paper/1603.03915/) (2016)
- [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://mlsanity.com/paper/2005.10977/) (2020)
- [Focusing Attention: Towards Accurate Text Recognition in Natural Images](https://mlsanity.com/paper/1709.02054/) (2017)
- [Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning](https://mlsanity.com/paper/2006.11476/) (2020)
- [Decoupled Attention Network for Text Recognition](https://mlsanity.com/paper/1912.10205/) (2019)
- [Detecting Text in Natural Image with Connectionist Text Proposal Network](https://mlsanity.com/paper/1609.03605/) (2016)
- [RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition](https://mlsanity.com/paper/2007.07542/) (2020)
- [Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning](https://mlsanity.com/paper/2001.00294/) (2020)
- [ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification](https://mlsanity.com/paper/1812.05824/) (2018)
- [Recursive Recurrent Nets with Attention Modeling for OCR in the Wild](https://mlsanity.com/paper/1603.03101/) (2016)
- [SCATTER: Selective Context Attentional Scene Text Recognizer](https://mlsanity.com/paper/2003.11288/) (2020)
- [TextScanner: Reading Characters in Order for Robust Scene Text Recognition](https://mlsanity.com/paper/1912.12422/) (2019)

## Built on by (most cited first, 30 of 41)

- [Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition](https://mlsanity.com/paper/2207.00193/) (2022)
- [TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control](https://mlsanity.com/paper/2410.10133/) (2024)
- [ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting](https://mlsanity.com/paper/2211.10578/) (2022)
- [Multi-Granularity Prediction for Scene Text Recognition](https://mlsanity.com/paper/2209.03592/) (2022)
- [Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features](https://mlsanity.com/paper/2111.15263/) (2021)
- [MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining](https://mlsanity.com/paper/2206.00311/) (2022)
- [Self-supervised Character-to-Character Distillation for Text Recognition](https://mlsanity.com/paper/2211.00288/) (2022)
- [Self-supervised Implicit Glyph Attention for Text Recognition](https://mlsanity.com/paper/2203.03382/) (2022)
- [Levenshtein OCR](https://mlsanity.com/paper/2209.03594/) (2022)
- [Context Perception Parallel Decoder for Scene Text Recognition](https://mlsanity.com/paper/2307.12270/) (2023)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [Instruction-Guided Scene Text Recognition](https://mlsanity.com/paper/2401.17851/) (2024)
- [LISTER: Neighbor Decoding for Length-Insensitive Scene Text Recognition](https://mlsanity.com/paper/2308.12774/) (2023)
- [Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning](https://mlsanity.com/paper/2308.07202/) (2023)
- [IPAD: Iterative, Parallel, and Diffusion-based Network for Scene Text Recognition](https://mlsanity.com/paper/2312.11923/) (2023)
- [Out of Length Text Recognition with Sub-String Matching](https://mlsanity.com/paper/2407.12317/) (2024)
- [Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition](https://mlsanity.com/paper/2402.13643/) (2024)
- [Pure Transformer with Integrated Experts for Scene Text Recognition](https://mlsanity.com/paper/2211.04963/) (2022)
- [Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach](https://mlsanity.com/paper/2308.08806/) (2023)
- [Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing](https://mlsanity.com/paper/2402.03082/) (2024)
- [Pushing the Performance Limit of Scene Text Recognizer without Human Annotation](https://mlsanity.com/paper/2204.07714/) (2022)
- [Multi-Granularity Prediction with Learnable Fusion for Scene Text Recognition](https://mlsanity.com/paper/2307.13244/) (2023)
- [Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval](https://mlsanity.com/paper/2408.00441/) (2024)
- [Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition](https://mlsanity.com/paper/2503.18746/) (2025)
- [TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model](https://mlsanity.com/paper/2403.10047/) (2024)
- [Towards Natural Language-Based Document Image Retrieval: New Dataset and Benchmark](https://mlsanity.com/paper/2512.20174/) (2025)
- [Masked and Permuted Implicit Context Learning for Scene Text Recognition](https://mlsanity.com/paper/2305.16172/) (2023)
- [PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution](https://mlsanity.com/paper/2311.17955/) (2023)
- [VL-Reader: Vision and Language Reconstructor is an Effective Scene Text Recognizer](https://mlsanity.com/paper/2409.11656/) (2024)
- [One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer](https://mlsanity.com/paper/2308.02770/) (2023)

## Abstract (arXiv)

Nowadays, scene text recognition has attracted more and more attention due to its various applications. Most state-of-the-art methods adopt an encoder-decoder framework with attention mechanism, which generates text autoregressively from left to right. Despite the convincing performance, the speed is limited because of the one-by-one decoding strategy. As opposed to autoregressive models, non-autoregressive models predict the results in parallel with a much shorter inference time, but the accuracy falls behind the autoregressive counterpart considerably. In this paper, we propose a Parallel, Iterative and Mimicking Network (PIMNet) to balance accuracy and efficiency. Specifically, PIMNet adopts a parallel attention mechanism to predict the text faster and an iterative generation mechanism to make the predictions more accurate. In each iteration, the context information is fully explored. To improve learning of the hidden layer, we exploit the mimicking learning in the training phase, where an additional autoregressive decoder is adopted and the parallel decoder mimics the autoregressive decoder with fitting outputs of the hidden layer. With the shared backbone between the two decoders, the proposed PIMNet can be trained end-to-end without pre-training. During inference, the branch of the autoregressive decoder is removed for a faster speed. Extensive experiments on public benchmarks demonstrate the effectiveness and efficiency of PIMNet. Our code will be available at https://github.com/Pay20Y/PIMNet.