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<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>bbOCR: An Open-source Multi-domain OCR Pipeline for Bengali Documents</title>
<meta name="description" content="">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta property="og:image" content="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/badlad-icon.png">
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<meta property="og:title" content="bbOCR: An Open-source Multi-domain OCR Pipeline for Bengali Documents" />
<meta property="og:description" content="Although many Optical Character Recognition(OCR) methods exit, the lack of comprehensive open-source systems
hampers the progress of document digitization in many low-
resource languages such as Bengali. The existing methods focus primarily on individual tasks such as word-level OCR,
document layout extraction, and distortion correction mostly
in high-resource languages. Unfortunately, for low-resource
languages, none is a practical system due to a lack of large-
scale datasets for different document OCR components, and
problems caused by an alphasyllabary writing system . Moreover, a system-level evaluation metric that takes into ac-
count the document layout and the text recognition simultaneously is an under-explored area. In this paper, we intro-
duce an open-source scalable document OCR system named
Bengali-BRACU-OCR (bbOCR) for reconstructing the Bengali documents into a structured searchable digitized format
considering the document layout, geometric distortions and
illumination variations . For building this pipeline, we provide two synthetic datasets and propose a customized model
for Bengali text recognition. Besides evaluating the system
at the component level, for a system-level extensive evaluation, we introduce a diversified evaluation dataset and com-
prehensive evaluation metrics. The extensive evaluation sug-
gests the practicality of our system over the state-of-the-art
open-source Bengali Document OCR system in terms of metrics and runtime. The source codes and datasets are available here." />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:title" content="bbOCR: An Open-source Multi-domain OCR Pipeline for Bengali Documents" />
<meta name="twitter:description" content="Although many Optical Character Recognition(OCR) methods exit, the lack of comprehensive open-source systems
hampers the progress of document digitization in many low-resource languages such as Bengali. The existing methods fo-
cus primarily on individual tasks such as word-level OCR,
document layout extraction, and distortion correction mostly
in high-resource languages. Unfortunately, for low-resource
languages, none is a practical system due to a lack of large-
scale datasets for different document OCR components, and
problems caused by an alphasyllabary writing system . Moreover, a system-level evaluation metric that takes into ac-
count the document layout and the text recognition simultaneously is an under-explored area. In this paper, we intro-
duce an open-source scalable document OCR system named
Bengali-BRACU-OCR (bbOCR) for reconstructing the Bengali documents into a structured searchable digitized format
considering the document layout, geometric distortions and
illumination variations . For building this pipeline, we provide two synthetic datasets and propose a customized model
for Bengali text recognition. Besides evaluating the system
at the component level, for a system-level extensive evaluation, we introduce a diversified evaluation dataset and com-
prehensive evaluation metrics. The extensive evaluation suggests the practicality of our system over the state-of-the-art
open-source Bengali Document OCR system in terms of metrics and runtime. The source codes and datasets are available
here." />
<meta name="twitter:image" content="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/badlad-icon.png" />
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</head>
<body>
<div class="container" id="main">
<div class="row">
<h2 class="col-md-12 text-center">
<b>bbOCR</b>: An Open-source Multi-domain OCR Pipeline for Bengali Documents</br>
<small>
arxiv 2023
</small>
</h2>
</div>
<div class="row">
<div class="col-md-12 text-center">
<ul class="list-inline">
<li>
<a href="https://dblp.org/pid/334/4024.html">
Imam Mohammad Zulkarnain
</a>
</li>
<li>
<a href="https://bucse.net/faculty_profile/76/shayekh_bin_islam">
Shayekh Bin Islam
</a>
</li>
<li>
<a href="https://nub.ac.bd/pims/faculty-member/9ajc4gof/md.-zami-al-zunaed-farabe">
Md. Zami Al Zunaed Farabe
</a>
</li>
<li>
<a href="https://sites.google.com/view/md-mehedi-hasan-shawon/home">
MD. Mehedi Hasan Shawon
</a>
</li>
<li>
<a href="https://scholar.google.com/citations?user=EaNBod8AAAAJ">
Jawaril Munshad Abedin
</a>
</li>
<li>
<a href="https://scholar.google.com/citations?user=Pt5C0gYAAAAJ">
Beig Rajibul Hasan
</a>
</li>
<li>
<a href="https://www.linkedin.com/in/marsia-haque-meghla-321021207/">
Marsia Meghla
</a>
</li>
<li>
<a href="https://www.linkedin.com/in/mobassir-hossen-916a83137/">
Syed Mobassir
</a>
</li>
<li>
<a href="https://mnansary.github.io/">
Nazmuddoha Ansary
</a>
</li>
<li>
<a href="https://people.bengali.ai/sushmit/">
Asif Sushmit
</a>
</li>
<li>
<a href="https://people.bengali.ai/farig/">
Farig Sadeq
</a>
</li>
</br>Bengali.AI
</ul>
</div>
</div>
<div class="row">
<div class="text-center">
<ul class="nav nav-pills nav-justified">
<li>
<a href="https://bengaliai.github.io/">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/logoBengaliai.jpg" height="60px">
<h4><strong>Bengali.AI Home</strong></h4>
</a>
</li>
<li>
<a href="https://arxiv.org/pdf/2308.10647.pdf">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/badladpaper.png" height="60px">
<h4><strong>Paper</strong></h4>
</a>
</li>
<li>
<a href="https://github.com/BengaliAI/bbocr">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/GitHub-Mark.png" height="60px">
<h4><strong>Github Repo</strong></h4>
</a>
</li>
<li>
<a href="https://www.kaggle.com/datasets/ocrteamriad/tfrs2-synthtiger-data">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/download.png" height="60px">
<h4><strong>bn-Synthtiger Dataset</strong></h4>
</a>
</li>
<li>
<a href="https://www.kaggle.com/datasets/ocrteamriad/tfrs2-synthindic">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/download.png" height="60px">
<h4><strong>bn-SynthIndic Dataset</strong></h4>
</a>
</li>
</ul>
</div>
</div>
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<video id="v0" width="100%" autoplay loop muted controls>
<source src="img/teaser.mp4" type="video/mp4" />
</video>
</div>
</div> -->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<div class="image">
<img src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/bbOCR.png" alt="Your Image", width = 750, height=300>
</div>
<div class="text">
<p>Figure: bbOCR reconstruction of sample document. The document has been analyzed and the OCR output has been converted to HTML format.</p>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Abstract
</h3>
<p class="text-justify">
Although many Optical Character Recognition(OCR) meth-
ods exit, the lack of comprehensive open-source systems
hampers the progress of document digitization in many low-
resource languages such as Bengali. The existing methods fo-
cus primarily on individual tasks such as word-level OCR,
document layout extraction, and distortion correction mostly
in high-resource languages. Unfortunately, for low-resource
languages, none is a practical system due to a lack of large-
scale datasets for different document OCR components, and
problems caused by an alphasyllabary writing system . More-
over, a system-level evaluation metric that takes into ac-
count the document layout and the text recognition simulta-
neously is an under-explored area. In this paper, we intro-
duce an open-source scalable document OCR system named
Bengali-BRACU-OCR (bbOCR) for reconstructing the Ben-
gali documents into a structured searchable digitized format
considering the document layout, geometric distortions and
illumination variations . For building this pipeline, we pro-
vide two synthetic datasets and propose a customized model
for Bengali text recognition. Besides evaluating the system
at the component level, for a system-level extensive evalua-
tion, we introduce a diversified evaluation dataset and com-
prehensive evaluation metrics. The extensive evaluation sug-
gests the practicality of our system over the state-of-the-art
open-source Bengali Document OCR system in terms of met-
rics and runtime. The source codes and datasets are available
here .
</p>
</div>
</div>
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Video
</h3>
<div class="text-center">
<div style="position:relative;padding-top:56.25%;">
<iframe src="https://youtube.com/embed/xrrhynRzC8k" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe>
</div>
</div>
</div>
</div> -->
<br>
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
360° Video Flythroughs
</h3>
<div class="text-center">
<div style="position:relative;padding-top:56.25%;">
<iframe src="https://www.youtube.com/embed/videoseries?list=PLzPoYEE6Aw7Jzjek1uEIPnpcTDL3u8tb4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe>
<!-- <iframe src="https://youtube.com/embed/jbE2ri8xEZo" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe> -->
</div>
</div>
</div> -->
<br>
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
XY aliasing
</h3>
<div class="video-compare-container" id="xyaliasDiv">
<video class="video" id="xyalias" loop playsinline autoPlay muted src="img/xy_alias_swipe_crf27.mp4" onplay="resizeAndPlay(this)"></video>
<canvas height=0 class="videoMerge" id="xyaliasMerge"></canvas>
</div>
<p class="text-justify">
A naive baseline (left) combining mip-NeRF 360 and Instant NGP results in aliasing as the camera moves laterally. Our full method (right) produces prefiltered renderings that do not flicker or shimmer.
</p>
</div>
</div>
-->
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Z aliasing
</h3>
<video id="v0" width="100%" autoplay loop muted controls>
<source src="img/z_alias_pdf_labeled.m4v" type="video/mp4" />
</video>
<p class="text-justify">
The proposal network used for resampling points along rays in mip-NeRF 360 results in an artifact we refer to as <em>z-aliasing</em>, where foreground content alternately appears and disappears as the camera moves toward or away from scene content. Z-aliasing occurs when the initial set of samples from the proposal network is not dense enough and misses thin structures, such as the chair above. Missed content can not be recovered by later rounds of sampling, since no future samples will be placed at that location along the ray. Our improvements to proposal network supervision result in a prefiltered proposal output that preserves the foreground object for all frames in this sequence. The plots above depict samples along a ray for three rounds of resampling (blue, orange, and green lines), with the y axis showing rendering weight (how much each interval contributes to the final rendered color), as a normalized probability density.
</p>
</div>
</div> -->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Citation
</h3>
<div class="form-group col-md-10 col-md-offset-1">
<textarea id="bibtex" class="form-control" readonly>
@misc{2308.10647,
Author = {Imam Mohammad Zulkarnain and Shayekh Bin Islam and Md. Zami Al Zunaed Farabe and Md. Mehedi Hasan Shawon and Jawaril Munshad Abedin and Beig Rajibul Hasan and Marsia Haque and Istiak Shihab and Syed Mobassir and MD. Nazmuddoha Ansary and Asif Sushmit and Farig Sadeque},
Title = {bbOCR: An Open-source Multi-domain OCR Pipeline for Bengali Documents},
Year = {2023},
Eprint = {arXiv:2308.10647},
}</textarea>
</div>
<div class="form-group col-md-10 col-md-offset-1">
<textarea id="bibtex" class="form-control" readonly>
@article{shihab2023badlad,
title={BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset},
author={Shihab, Md and Hossain, Istiak and Hasan, Md and Emon, Mahfuzur Rahman and Hossen, Syed Mobassir and Ansary, Md and Ahmed, Intesur and Rakib, Fazle Rabbi and Dhruvo, Shahriar Elahi and Dip, Souhardya Saha and others},
journal={arXiv preprint arXiv:2303.05325},
year={2023}
}</textarea>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Annotators
</h3>
<p class="text-justify">
Marshia Haque Meghla, Junayed Bhuiyan.
<br><br>
.
</p>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Acknowledgements
</h3>
<p class="text-justify">
We are thankful to Center for Bangladesh Genocide Research - CBGR for sharing some invaluable
historical documents for this dataset. We also thank APSIS Solutions for sharing and opensourcing their word-recognition models and data generation strategies.
<br><br>
.
</p>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Contact
</h3>
<p class="text-justify">
<br><br>
.
</p>
</div>
</div>
</div>
</body>
</html>