Abstract

Abstract—Automated evaluation of handwritten answers hasbeen a challenging problem for scaling the education systemfor many years. Speeding up the evaluation remains as themajor bottleneck for enhancing the throughput of instructors.This paper describes an effective method for automaticallyevaluating the short descriptive handwritten answers fromthe digitized images. Our goal is to evaluate a student’shandwritten answer by assigning an evaluation score that iscomparable to the human-assigned scores. Existing works inthis domain mainly focused on evaluating handwritten essayswith handcrafted, non-semantic features. Our contribution istwo-fold: 1) we model this problem as a self-supervised, feature-based classification problem, which can fine-tune itself for eachquestion without any explicit supervision. 2) We introduce theusage of semantic analysis for auto-evaluation in handwrittentext space using the combination of Information Retrievaland Extraction (IRE) and, Natural Language Processing (NLP)methods to derive a set of useful features. We tested our methodon three datasets created from various domains, using the helpof students of different age groups. Experiments show that ourmethod performs comparably to that of human evaluators.

Abstract

bstract—Graphical elements: particularly tables and fig-ures contain a visual summary of the most valuable infor-mation contained in a document. Therefore, localization ofsuch graphical objects in the document images is the initialstep to understand the content of such graphical objects ordocument images. In this paper, we present a novel end-to-endtrainable deep learning based framework to localize graphicalobjects in the document images called as Graphical ObjectDetection (GOD). Our framework is data-driven and does notrequire any heuristics or meta-data to locate graphical objectsin the document images. TheGODexplores the concept oftransfer learning and domain adaptation to handle scarcity oflabeled training images for graphical object detection task inthe document images. Performance analysis carried out on thevarious public benchmark data sets:ICDAR-2013,ICDAR-POD2017 andUNLVshows that our model yields promising resultsas compared to state-of-the-art techniques

Abstract

bstract—Reading of mathematical expression or equationin the document images is very challenging due to the largevariability of mathematical symbols and expressions. In thispaper, we pose reading of mathematical equation as a taskof generation of the textual description which interprets theinternal meaning of this equation. Inspired by the naturalimage captioning problem in computer vision, we presenta mathematical equation description (MED) model, a novelend-to-end trainable deep neural network based approachthat learns to generate a textual description for readingmathematical equation images. OurMEDmodel consists of aconvolution neural network as an encoder that extracts featuresof input mathematical equation images and a recurrent neuralnetwork with attention mechanism which generates descriptionrelated to the input mathematical equation images. Due tothe unavailability of mathematical equation image data setswith their textual descriptions, we generate two data sets forexperimental purpose. To validate the effectiveness of ourMEDmodel, we conduct a real-world experiment to see whether thestudents are able to write equations by only reading or listeningtheir textual descriptions or not. Experiments conclude that thestudents are able to write most of the equations correctly byreading their textual descriptions only.

Abstract

A Cost Efficient Approach to Correct OCR Errors in Large Document CollectionsDeepayan Das, Jerin Philip, Minesh Mathew and C. V. JawaharCenter for Visual Information Technology, IIIT Hyderabad, India.{deepayan.das, jerin.philip, minesh.mathew}@research.iiit.ac.in, jawahar@iiit.ac.inAbstract—Word error rate of anOCRis often higher thanits character error rate. This is especially true whenOCRsaredesigned by recognizing characters. High word accuracies arecritical for many practical applications like content creationand text-to-speech systems. In order to detect and correct themisrecognised words, it is common for anOCRto employ apost-processor module to improve the word accuracy. However,conventional approaches to post-processing like looking up adictionary or using a statistical language model (SLM), are stilllimited. In many such scenarios, it is often required to removethe outstanding errors manually.We observe that the traditional post-processing schemes lookat error words sequentially sinceOCRs process documents oneat a time. We propose a cost efficient model to address the errorwords in batches rather than correcting them individually.We exploit the fact that a collection of documents (eg. abook), unlike a single document, has a structure leading torepetition of words. Such words, if efficiently grouped togetherand corrected together, can lead to significant reduction in theeffort. Error correction can be fully automatic or with a humanin the loop. We compare the performance of our method withvarious baseline approaches including the case where all theerrors are removed by a human. We demonstrate the efficacy ofour solution empirically by reporting more than70%reductionin the human effort with near perfect error correction. Wevalidate our method on books in both English and Hindi.

Abstract

We present a simple, yet effective, Neural Ma-chineTranslationsystemforIndianlanguages.Wedemonstratethefeasibilityformultiplelan-guage pairs, and establish a strong baseline forfurther research.

Abstract

Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (CNN’s) trained on whole-slide images distinguish clear cell and chromophobe RCC from normal tissue with a classification accuracy of 93.39% and 87.34%, respectively. Further, a CNN trained to distinguish clear cell, chromophobe and papillary RCC achieves a classification accuracy of 94.07%. Here, we introduced a novel support vector machine-based method that helped to break the multi-class classification task into multiple binary classification tasks which not only improved the performance of the model but also helped to deal with data imbalance. Finally, we extracted the morphological features from high probability tumor regions identified by the CNN to predict patient survival outcome of most common clear cell RCC. The generated risk index based on both tumor shape and nuclei features are significantly associated with patient survival outcome. These results highlight that deep learning can play a role in both cancer diagnosis and prognosis.

Abstract

Cross-modal retrieval methods have been significantly improvedin last years with the use of deep neural networks and large-scaleannotated datasets such as ImageNet and Places. However, collect-ing and annotating such datasets requires a tremendous amountof human effort and, besides, their annotations are limited to dis-crete sets of popular visual classes that may not be representativeof the richer semantics found on large-scale cross-modal retrievaldatasets. In this paper, we present a self-supervised cross-modalretrieval framework that leverages as training data the correlationsbetween images and text on the entire set of Wikipedia articles.Our method consists in training a CNN to predict: (1) the semanticcontext of the article in which an image is more probable to appearas an illustration, and (2) the semantic context of its caption. Ourexperiments demonstrate that the proposed method is not only ca-pable of learning discriminative visual representations for solvingvision tasks like classification, but that the learned representationsare better for cross-modal retrieval when compared to supervisedpre-training of the network on the ImageNet dataset.

Abstract

Abstract—This paper presents final results of ICDAR 2019Scene Text Visual Question Answering competition (ST-VQA).ST-VQA introduces an important aspect that is not addressedby any Visual Question Answering system up to date, namelythe incorporation of scene text to answer questions asked aboutan image. The competition introduces a new dataset comprising23,038images annotated with31,791question / answer pairswhere the answer is always grounded on text instances presentin the image. The images are taken from7different publiccomputer vision datasets, covering a wide range of scenarios.The competition was structured in three tasks of increas-ing difficulty, that require reading the text in a scene andunderstanding it in the context of the scene, to correctlyanswer a given question. A novel evaluation metric is presented,which elegantly assesses both key capabilities expected from anoptimal model: text recognition and image understanding.A detailed analysis of results from different participants isshowcased, which provides insight into the current capabilitiesof VQA systems that can read. We firmly believe the datasetproposed in this challenge will be an important milestone toconsider towards a path of more robust and general models thatcan exploit scene text to achieve holistic image understanding.

Abstract

AbstractFully supervised deep learning-based methods have created a profound im-pact in various fields of computer science. Compared to classical methods, superviseddeep learning-based techniques face scalability issues as they require huge amountsof labeled data and, more significantly, are unable to generalize to multiple domainsand tasks. In recent years, a lot of research has been targeted towards addressingthese issues within the deep learning community. Although there have been extensivesurveys on learning paradigms such as semi-supervised and unsupervised learning,there are few timely reviews after the emergence of deep learning. In this paper, weprovide an overview of the contemporary literature surrounding alternatives to fullysupervised learning in the deep learning context. First, we summarize the relevanttechniques that fall between the paradigm of supervised and unsupervised learning.Second, we take autonomous navigation as a running example to explain and com-pare different models. Finally, we highlight some shortcomings of current methodsand suggest future directions.

Abstract

Abstract— Driver inattention is one of the leading causesof vehicle crashes and incidents worldwide. Driver inattentionincludes driver fatigue leading to drowsiness and driver distrac-tion, say due to use of cellphone or rubbernecking, all of whichleads to a lack of situational awareness. Hitherto, techniquespresented to monitor driver attention evaluated factors suchas fatigue and distraction independently. However, in orderto develop a robust driver attention monitoring system allthe factors affecting driver’s attention needs to be analyzedholistically. In this paper, we presentAutoRate, a system thatleverages front camera of a windshield-mounted smartphoneto monitor driver’s attention by combining several features.We derive a driver attention rating by fusing spatio-temporalfeatures based on the driver state and behavior such as headpose, eye gaze, eye closure, yawns, use of cellphones, etc.We perform extensive evaluation ofAutoRateon real-world driving data and also data from controlled, static vehiclesettings with 30 drivers in a large city. We compareAutoRate’sautomatically-generated rating with the scores given by 5human annotators. Further, we compute the agreement betweenAutoRate’s rating and human annotator rating using kappacoefficient.AutoRate’s automatically-generated rating has anoverall agreement of 0.87 with the ratings provided by 5 humanannotators on the static dataset.