Contextual Attention Localization for Offline Handwritten Text Recognition

Related tags

Deep LearningCALText
Overview

CALText

This repository contains the source code for CALText model introduced in "CALText: Contextual Attention Localization for Offline Handwritten Text" paper. The details of this model are presented in: (Add paper link)

image image

Samples of the datasets that were used to train and test the model can be found at: http://faculty.pucit.edu.pk/nazarkhan/work/urdu_ohtr/pucit_ohul_dataset.html

The code in this model was based on the work of:

https://github.com/JianshuZhang/WAP.

https://github.com/wwjwhen/Watch-Attend-and-Parse-tensorflow-version.

Requirements

Python 3 Tensorflow v1.6

Usage

Upload data files into your Colab account, create pickle files (train, valid, and test images and labels) from the dataset. You can place the pickle dataset files at any folder of your preference but change the path settings in the code where this data is being loaded.

Run "makepickle.ipynb" to create pickle files for train and test data. Further distribute the train pickle file into train and valid pickle files by using last 907 images and labels of train as valid.

For training, set mode="train", and run "CALText.ipynb".

For testing, set mode="test", and run "CALText.ipynb".

For Contextual Attention, set alpha_reg=0, while training and testing.

For Contextual Attention Localization, set alpha_reg=1, while training and testing.

Run on Python Compiler

To run the code on python compiler, copy the code and make file as "makepickle.py" and "CALText.py". Use following commands to run code files.

python makepickle.py

python CALText.py

Run on Google Colab

Open "makepickle.ipynb" and "CALText.ipynb" notebook in Google Colab Notebook, and run.

Run "%tensorflow_version 1.x" command at colab notebook before running of "CALText.ipynb".

Change runtime to GPU or TPU for better performance.

Add these lines in notebook for accessing data from google derive:

from google.colab import drive

drive.mount("/gdrive", force_remount=True)

References

PUCIT Offline Handwritten Urdu Lines (PUCIT-OHUL) Dataset: http://faculty.pucit.edu.pk/nazarkhan/work/urdu_ohtr/pucit_ohul_dataset.html

Previous Work:

http://faculty.pucit.edu.pk/nazarkhan/work/urdu_ohtr/index.html

http://faculty.pucit.edu.pk/nazarkhan/work/urdu_ohtr/ICFHR2020_manuscript.pdf

The code used for the free [email protected] Webinar series on Reinforcement Learning in Finance

Reinforcement Learning in Finance [email protected] Webinar This repository provides the code f

Yves Hilpisch 62 Dec 22, 2022
Code repository accompanying the paper "On Adversarial Robustness: A Neural Architecture Search perspective"

On Adversarial Robustness: A Neural Architecture Search perspective Preparation: Clone the repository: https://github.com/tdchaitanya/nas-robustness.g

Chaitanya Devaguptapu 4 Nov 10, 2022
Efficient 3D human pose estimation in video using 2D keypoint trajectories

3D human pose estimation in video with temporal convolutions and semi-supervised training This is the implementation of the approach described in the

Meta Research 3.1k Dec 29, 2022
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection

LiDAR Distillation Paper | Model LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection Yi Wei, Zibu Wei, Yongming Rao, Jiax

Yi Wei 75 Dec 22, 2022
Systemic Evolutionary Chemical Space Exploration for Drug Discovery

SECSE SECSE: Systemic Evolutionary Chemical Space Explorer Chemical space exploration is a major task of the hit-finding process during the pursuit of

64 Dec 16, 2022
RoMA: Robust Model Adaptation for Offline Model-based Optimization

RoMA: Robust Model Adaptation for Offline Model-based Optimization Implementation of RoMA: Robust Model Adaptation for Offline Model-based Optimizatio

9 Oct 31, 2022
DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.

DeepLab Introduction DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe. It combines densely-compute

Ali 234 Nov 14, 2022
Efficient 6-DoF Grasp Generation in Cluttered Scenes

Contact-GraspNet Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, Dieter

NVIDIA Research Projects 148 Dec 28, 2022
Blender Python - Node-based multi-line text and image flowchart

MindMapper v0.8 Node-based text and image flowchart for Blender Mindmap with shortcuts visible: Mindmap with shortcuts hidden: Notes This was requeste

SpectralVectors 58 Oct 08, 2022
Code for our CVPR 2022 Paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection"

GEN-VLKT Code for our CVPR 2022 paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection". Contributed by Yue Lia

Yue Liao 47 Dec 04, 2022
Auxiliary Raw Net (ARawNet) is a ASVSpoof detection model taking both raw waveform and handcrafted features as inputs, to balance the trade-off between performance and model complexity.

Overview This repository is an implementation of the Auxiliary Raw Net (ARawNet), which is ASVSpoof detection system taking both raw waveform and hand

6 Jul 08, 2022
Self-Supervised Learning

Self-Supervised Learning Features self_supervised offers features like modular framework support for multi-gpu training using PyTorch Lightning easy t

Robin 1 Dec 14, 2021
JAXDL: JAX (Flax) Deep Learning Library

JAXDL: JAX (Flax) Deep Learning Library Simple and clean JAX/Flax deep learning algorithm implementations: Soft-Actor-Critic (arXiv:1812.05905) Transf

Patrick Hart 4 Nov 27, 2022
An implementation of the WHATWG URL Standard in JavaScript

whatwg-url whatwg-url is a full implementation of the WHATWG URL Standard. It can be used standalone, but it also exposes a lot of the internal algori

314 Dec 28, 2022
Bulk2Space is a spatial deconvolution method based on deep learning frameworks

Bulk2Space Spatially resolved single-cell deconvolution of bulk transcriptomes using Bulk2Space Bulk2Space is a spatial deconvolution method based on

Dr. FAN, Xiaohui 60 Dec 27, 2022
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)

[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio

42 Nov 01, 2022
Wind Speed Prediction using LSTMs in PyTorch

Implementation of Deep-Forecast using PyTorch Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting Adapted from original implementation Setu

Onur Kaplan 151 Dec 14, 2022
The implementation of ICASSP 2020 paper "Pixel-level self-paced learning for super-resolution"

Pixel-level Self-Paced Learning for Super-Resolution This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resoluti

Elon Lin 41 Dec 15, 2022
PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning This is the PyTorch implementation of our paper: FeatMatch: Feature-Based Augmentat

43 Nov 19, 2022
End-to-End Referring Video Object Segmentation with Multimodal Transformers

End-to-End Referring Video Object Segmentation with Multimodal Transformers This repo contains the official implementation of the paper: End-to-End Re

608 Dec 30, 2022