arXiv subjects

Rajesh Shreedhar Bhat

Publications and source records attributed to Rajesh Shreedhar Bhat.

4 recordsLinked to original sources

arXiv2019

Semi-Bagging Based Deep Neural Architecture to Extract Text from High Entropy Images

Extracting texts of various size and shape from images containing multiple objects is an important problem in many contexts, especially, in connection to e-commerce, augmented reality assistance system in natural scene, etc. The existing works (based on only CNN) often perform sub-optimally when the image contains regions of high entropy having multiple objects. This paper presents an end-to-end text detection strategy combining a segmentation algorithm and an ensemble of multiple text detectors of different types to detect text in every individual image segments independently. The proposed strategy involves a super-pixel based image segmenter which splits an image into multiple regions. A convolutional deep neural architecture is developed which works on each of the segments and detects texts of multiple shapes, sizes, and structures. It outperforms the competing methods in terms of coverage in detecting texts in images especially the ones where the text of various types and sizes are compacted in a small region along with various other objects. Furthermore, the proposed text detection method along with a text recognizer outperforms the existing state-of-the-art approaches in extracting text from high entropy images. We validate the results on a dataset consisting of product images on an e-commerce website.

cs.CV↗

arXiv2018

Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit

We propose a post-OCR text correction approach for digitising texts in Romanised Sanskrit. Owing to the lack of resources our approach uses OCR models trained for other languages written in Roman. Currently, there exists no dataset available for Romanised Sanskrit OCR. So, we bootstrap a dataset of 430 images, scanned in two different settings and their corresponding ground truth. For training, we synthetically generate training images for both the settings. We find that the use of copying mechanism (Gu et al., 2016) yields a percentage increase of 7.69 in Character Recognition Rate (CRR) than the current state of the art model in solving monotone sequence-to-sequence tasks (Schnober et al., 2016). We find that our system is robust in combating OCR-prone errors, as it obtains a CRR of 87.01% from an OCR output with CRR of 35.76% for one of the dataset settings. A human judgment survey performed on the models shows that our proposed model results in predictions which are faster to comprehend and faster to improve for a human than the other systems.

cs.CL↗

arXiv2015

Ecosystem: A Characteristic Of Crowdsourced Environments

The phenomenal success of certain crowdsourced online platforms, such as Wikipedia, is accredited to their ability to tap the crowd's potential to collaboratively build knowledge. While it is well known that the crowd's collective wisdom surpasses the cumulative individual expertise, little is understood on the dynamics of knowledge building in a crowdsourced environment. A proper understanding of the dynamics of knowledge building in a crowdsourced environment would enable one in the better designing of such environments to solicit knowledge from the crowd. Our experiment on crowdsourced systems based on annotations shows that an important reason for the rapid knowledge building in such environments is due to variance in expertise. First, we used as our test bed, a customized Crowdsourced Annotation System (CAS) which provides a group of users the facility to annotate a given document while trying to understand it. Our results showed the presence of different genres of proficiency amongst the users of an annotation system. We observed that the ecosystem in crowdsourced annotation system comprised of mainly four categories of contributors, namely: Probers, Solvers, Articulators and Explorers. We inferred from our experiment that the knowledge garnering mainly happens due to the synergetic interaction across these categories. Further, we conducted an analysis on the dataset of Wikipedia and Stack Overflow and noticed the ecosystem presence in these portals as well. From this study, we claim that the ecosystem is a universal characteristic of all crowdsourced portals.

cs.CY↗

arXiv2015

A Framework for Textbook Enhancement and Learning using Crowdsourced Annotations

Despite a significant improvement in the educational aids in terms of effective teaching-learning process, most of the educational content available to the students is less than optimal in the context of being up-to-date, exhaustive and easy-to-understand. There is a need to iteratively improve the educational material based on the feedback collected from the students' learning experience. This can be achieved by observing the students' interactions with the content, and then having the authors modify it based on this feedback. Hence, we aim to facilitate and promote communication between the communities of authors, instructors and students in order to gradually improve the educational material. Such a system will also help in students' learning process by encouraging student-to-student teaching. Underpinning these objectives, we provide the framework of a platform named Crowdsourced Annotation System (CAS) where the people from these communities can collaborate and benefit from each other. We use the concept of in-context annotations, through which, the students can add their comments about the given text while learning it. An experiment was conducted on 60 students who try to learn an article of a textbook by annotating it for four days. According to the result of the experiment, most of the students were highly satisfied with the use of CAS. They stated that the system is extremely useful for learning and they would like to use it for learning other concepts in future.

cs.CY↗