ashishpapanai / parseq-ov

Scene Text Recognition with Permuted Autoregressive Sequence Models (ECCV 2022)

Scene Text Recognition with
Permuted Autoregressive Sequence Models

Darwin Bautista and Rowel Atienza

Electrical and Electronics Engineering Institute
University of the Philippines, Diliman

Method | Sample Results | Getting Started | FAQ | Training | Evaluation | Citation

Scene Text Recognition (STR) models use language context to be more robust against noisy or corrupted images. Recent approaches like ABINet use a standalone or external Language Model (LM) for prediction refinement. In this work, we show that the external LM—which requires upfront allocation of dedicated compute capacity—is inefficient for STR due to its poor performance vs cost characteristics. We propose a more efficient approach using permuted autoregressive sequence (PARSeq) models.

NOTE: P-S and P-Ti are shorthands for PARSeq-S and PARSeq-Ti, respectively.

Method tl;dr

Our main insight is that with an ensemble of autoregressive (AR) models, we could unify the current STR decoding methods (context-aware AR and context-free non-AR) and the bidirectional (cloze) refinement model:

A single Transformer can realize different models by merely varying its attention masks. This characteristic coupled with Permutation Language Modeling allows for a unified STR model capable of context-free and context-aware inference, as well as iterative prediction refinement using bidirectional context without requiring a standalone language model. PARSeq can be considered an ensemble of AR models with shared architecture and weights:

Sample Results

Input Image	PARSeq-SA	ABINet	TRBA	ViTSTR-S	CRNN
	CHEWBACCA	CHEWBAGGA	CHEWBACCA	CHEWBACCA	CHEWUACCA
	Chevrol	Chevro_	Chevro_	Chevr__	Chevr__
	SALMON	SALMON	SALMON	SALMON	SA_MON
	Verbandsteffe	Verbandsteffe	Verbandstelle	Verbandsteffe	Verbandsleffe
	Kappa	Kappa	Kaspa	Kappa	Kaada
	3rdAve	3=-Ave	3rdAve	3rdAve	Coke

NOTE: Bold letters and underscores indicate wrong and missing character predictions, respectively.

Getting Started

This repository contains the reference implementation for PARSeq and reproduced models (collectively referred to as Scene Text Recognition Model Hub). See NOTICE for copyright information. Majority of the code is licensed under the Apache License v2.0 (see LICENSE) while ABINet and CRNN sources are released under the BSD and MIT licenses, respectively (see corresponding LICENSE files for details).

Demo

An interactive Gradio demo hosted at Hugging Face is available. The pretrained weights released here are used for the demo.

Installation

Requires Python 3.7 and PyTorch 1.10 or newer. Tested on Python 3.9 and PyTorch 1.10.

$ pip install -r requirements.txt
$ pip install -e .

Datasets

Download the datasets from the following links:

1. LMDB archives for MJSynth, SynthText, IIIT5k, SVT, SVTP, IC13, IC15, CUTE80, ArT, RCTW17, ReCTS, LSVT, MLT19, COCO-Text, and Uber-Text.
2. LMDB archives for TextOCR and OpenVINO.

Pretrained Models via Torch Hub

Available models are: abinet, crnn, trba, vitstr, parseq_tiny, and parseq.

import torch
from PIL import Image
from strhub.data.module import SceneTextDataModule

# Load model and image transforms
parseq = torch.hub.load('baudm/parseq', 'parseq', pretrained=True).eval()
img_transform = SceneTextDataModule.get_transform(parseq.hparams.img_size)

img = Image.open('/path/to/image.png').convert('RGB')
# Preprocess. Model expects a batch of images with shape: (B, C, H, W)
img = img_transform(img).unsqueeze(0)

logits = parseq(img)
logits.shape  # torch.Size([1, 26, 95]), 94 characters + [EOS] symbol

# Greedy decoding
pred = logits.softmax(-1)
label, confidence = parseq.tokenizer.decode(pred)
print('Decoded label = {}'.format(label[0]))

Frequently Asked Questions

• How do I train on a new language? See Issues #5 and #9.
• Can you export to TorchScript or ONNX? Yes, with a caveat (Issue #12).
• How do I test on my own dataset? See Issue #27.
• How do I finetune and/or create a custom dataset? See Issue #7.
• What is val_NED? See Issue #10.

Training

The training script can train any supported model. You can override any configuration using the command line. Please refer to Hydra docs for more info about the syntax. Use ./train.py --help to see the default configuration.

Sample commands for different training configurations

Finetune using pretrained weights

./train.py pretrained=parseq-tiny  # Not all experiments have pretrained weights

Train a model variant/preconfigured experiment

The base model configurations are in configs/model/, while variations are stored in configs/experiment/.

./train.py +experiment=parseq-tiny  # Some examples: abinet-sv, trbc

Specify the character set for training

./train.py charset=94_full  # Other options: 36_lowercase or 62_mixed-case. See configs/charset/

Specify the training dataset

./train.py dataset=real  # Other option: synth. See configs/dataset/

Change general model training parameters

./train.py model.img_size=[32, 128] model.max_label_length=25 model.batch_size=384

Change data-related training parameters

./train.py data.root_dir=data data.num_workers=2 data.augment=true

Change pytorch_lightning.Trainer parameters

./train.py trainer.max_epochs=20 trainer.gpus=2 +trainer.accelerator=gpu

Note that you can pass any Trainer parameter, you just need to prefix it with + if it is not originally specified in configs/main.yaml.

Resume training from checkpoint (experimental)

./train.py +experiment=<model_exp> ckpt_path=outputs/<model>/<timestamp>/checkpoints/<checkpoint>.ckpt

Evaluation

The test script, test.py, can be used to evaluate any model trained with this project. For more info, see ./test.py --help.

PARSeq runtime parameters can be passed using the format param:type=value. For example, PARSeq NAR decoding can be invoked via ./test.py parseq.ckpt refine_iters:int=2 decode_ar:bool=false.

Sample commands for reproducing results

Lowercase alphanumeric comparison on benchmark datasets (Table 6)

./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt  # or use the released weights: ./test.py pretrained=parseq

Sample output:

Dataset	# samples	Accuracy	1 - NED	Confidence	Label Length
IIIT5k	3000	99.00	99.79	97.09	5.09
SVT	647	97.84	99.54	95.87	5.86
IC13_1015	1015	98.13	99.43	97.19	5.31
IC15_2077	2077	89.22	96.43	91.91	5.33
SVTP	645	96.90	99.36	94.37	5.86
CUTE80	288	98.61	99.80	96.43	5.53
Combined	7672	95.95	98.78	95.34	5.33

---

Benchmark using different evaluation character sets (Table 4)

./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt  # lowercase alphanumeric (36-character set)
./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt --cased  # mixed-case alphanumeric (62-character set)
./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt --cased --punctuation  # mixed-case alphanumeric + punctuation (94-character set)

Lowercase alphanumeric comparison on more challenging datasets (Table 5)

./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt --new

Benchmark Model Compute Requirements (Figure 5)

./bench.py model=parseq model.decode_ar=false model.refine_iters=3
<torch.utils.benchmark.utils.common.Measurement object at 0x7f8fcae67ee0>
model(x)
  Median: 14.87 ms
  IQR:    0.33 ms (14.78 to 15.12)
  7 measurements, 10 runs per measurement, 1 thread
| module                | #parameters   | #flops   | #activations   |
|:----------------------|:--------------|:---------|:---------------|
| model                 | 23.833M       | 3.255G   | 8.214M         |
|  encoder              |  21.381M      |  2.88G   |  7.127M        |
|  decoder              |  2.368M       |  0.371G  |  1.078M        |
|  head                 |  36.575K      |  3.794M  |  9.88K         |
|  text_embed.embedding |  37.248K      |  0       |  0             |

Latency Measurements vs Output Label Length (Appendix I)

./bench.py model=parseq model.decode_ar=false model.refine_iters=3 +range=true

Orientation robustness benchmark (Appendix J)

./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt --cased --punctuation  # no rotation
./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt --cased --punctuation --rotation 90
./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt --cased --punctuation --rotation 180
./test.py outputs/<model>/<timestamp>/checkpoints/last.ckpt --cased --punctuation --rotation 270

Using trained models to read text from images (Appendix L)

./read.py outputs/<model>/<timestamp>/checkpoints/last.ckpt --images demo_images/*  # Or use ./read.py pretrained=parseq
Additional keyword arguments: {}
demo_images/art-01107.jpg: CHEWBACCA
demo_images/coco-1166773.jpg: Chevrol
demo_images/cute-184.jpg: SALMON
demo_images/ic13_word_256.png: Verbandsteffe
demo_images/ic15_word_26.png: Kaopa
demo_images/uber-27491.jpg: 3rdAve

# use NAR decoding + 2 refinement iterations for PARSeq
./read.py pretrained=parseq refine_iters:int=2 decode_ar:bool=false --images demo_images/*

Tuning

We use Ray Tune for automated parameter tuning of the learning rate. See ./tune.py --help. Extend tune.py to support tuning of other hyperparameters.

./tune.py tune.num_samples=20  # find optimum LR for PARSeq's default config using 20 trials
./tune.py +experiment=tune_abinet-lm  # find the optimum learning rate for ABINet's language model

Citation

@InProceedings{bautista2022parseq,
  author={Bautista, Darwin and Atienza, Rowel},
  title={Scene Text Recognition with Permuted Autoregressive Sequence Models},
  booktitle={Proceedings of the 17th European Conference on Computer Vision (ECCV)},
  month={10},
  year={2022},
  publisher={Springer International Publishing},
  address={Cham}
}