Rajesh Shreedhar Bhat

Pranay Dugar, Anirban Chatterjee, Rajesh Shreedhar Bhat et al.

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.

Amrith Krishna, Bodhisattwa Prasad Majumder, Rajesh Shreedhar Bhat et al.

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.

Anamika Chhabra, S. R. S. Iyengar, Poonam Saini et al.

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.