TEXTRON: Weakly Supervised Multilingual Text Detection through Data
  Programming
Dhruv Kudale Badri Vishal Kasuba Venkatapathy Subramanian Parag Chaudhuri Ganesh Ramakrishnan
Several recent deep learning (DL) based techniques perform considerably well
on image-based multilingual text detection. However, their performance relies
heavily on the availability and quality of training data. There are numerous
types of page-level document images consisting of information in several
modalities, languages, fonts, and layouts. This makes text detection a
challenging problem in the field of computer vision (CV), especially for
low-resource or handwritten languages. Furthermore, there is a scarcity of
word-level labeled data for text detection, especially for multilingual
settings and Indian scripts that incorporate both printed and handwritten text.
Conventionally, Indian script text detection requires training a DL model on
plenty of labeled data, but to the best of our knowledge, no relevant datasets
are available. Manual annotation of such data requires a lot of time, effort,
and expertise. In order to solve this problem, we propose TEXTRON, a Data
Programming-based approach, where users can plug various text detection methods
into a weak supervision-based learning framework. One can view this approach to
multilingual text detection as an ensemble of different CV-based techniques and
DL approaches. TEXTRON can leverage the predictions of DL models pre-trained on
a significant amount of language data in conjunction with CV-based methods to
improve text detection in other languages. We demonstrate that TEXTRON can
improve the detection performance for documents written in Indian languages,
despite the absence of corresponding labeled data. Further, through extensive
experimentation, we show improvement brought about by our approach over the
current State-of-the-art (SOTA) models, especially for handwritten Devanagari
text. Code and dataset has been made available at
https://github.com/IITB-LEAP-OCR/TEXTRON