Abstract

Recognition of text on word or line images, without the need for sub-word segmentation has become the mainstream of research and development of text recognition for Indian languages. Modelling unsegmented sequences using Connectionist Temporal Classification (CTC) is the most commonly used approach for segmentation-free OCR. In this work we present a comprehensive empirical study of various neural network models that uses CTC for transcribing step-wise predictions in the neural network output to a Unicode sequence. The study is conducted for 13 Indian languages, using an internal dataset that has around 1000 pages per language. We study the choice of line vs word as the recognition unit, and use of synthetic data to train the models. We compare our models with popular publicly available OCR tools for end-to-end document image recognition. Our end-to-end pipeline that employ our recognition models and existing text segmentation tools outperform these public OCR tools for 8 out of the 13 languages. We also introduce a new public dataset called Mozhi for word and line recognition in Indian language. The dataset contains more than 1.2 million annotated word images (120 thousand text lines) across 13 Indian languages. Our code, trained models and the Mozhi dataset will be made available at cvit-projects Additional Key Words and Phrases: indic ocr, indian languages, crnn, ctc, text recognition

Abstract

A system and method generating an optimized medical image using a machine learning model are provided . The method includes ( i ) receiving one or more medical images , ( ii ) segmenting to generate a transformed medical image for detecting a plurality of target elements , ( iii ) displaying the transformed medical image , ( iv ) receiving markings and scribblings associated with scribble locations from a user , ( v ) identifying errors associated with an outline of a target element , ( vi ) computing a loss function for a location of pixels where the target element is located on the transformed medical image , ( vii ) modifying the pre - defined weights ( w ) to match the segmentation output and the determined target element ,( viii ) determining whether the segmentation output is matched with the target element and ( ix ) generating the optimized medical image if the segmentation output is matched with the determined target element

Abstract

We present a Nash equilibrium analysis for single-buyer singleseller multi-unit 𝑘-double auctions for scaling-based bidding strategies. We then design a Deep Deterministic Policy Gradient (DDPG) based learning strategy, DDPGBBS, for a participating agent to suggest bids that approximately achieve the above Nash equilibrium. We expand DDPGBBS to be helpful in more complex settings with multiple buyers/sellers trading multiple units in a Periodic Double Auction (PDA), such as the wholesale market in smart-grids. We demonstrate the efficacy of DDPGBBS with Power Trading Agent Competition’s (PowerTAC) wholesale market PDA as a testbed.

Abstract

A system and method for generating an optimized image with scribble - based interactive image segmentation model using a machine learning are provided . The method includes , ( i ) segmenting , using a machine learning model , an image to classify into classes each class is represented with a label , ( ii ) displaying the classified image which specifies the classes on the classified image with outlines , ( iii ) enabling a user to scribble on the classified image to annotate the classes if an area is not classified , ( iv ) assigning a color mask for each scribbled area , ( v ) computing , using the machine learning model , a loss function for a location of pixels based on color mask , ( vi ) modifying pre - defined weights for each scribbled area to match the annotated image and a determined class on the classified image , and ( vii ) generating the optimized image if the annotated image is matched with the determined class on the classified image

Abstract

Keyword Spotting (KWS) detects a set of pre-defined spoken keywords. Building a KWS system for an arbitrary set requires massive training datasets. We propose to use the text transcripts from an Automatic Speech Recognition (ASR) system alongside triplets for KWS training. The intermediate representation from the ASR system trained on a speech corpus is used as acoustic word embeddings for keywords. Triplet loss is added to the Connectionist Temporal Classification (CTC) loss in the ASR while training. This method achieves an Average Precision (AP) of 0.843 over 344 words unseen by the model trained on the TIMIT dataset. In contrast, the Multi-View recurrent method that learns jointly on the text and acoustic embeddings achieves only 0.218 for out-of-vocabulary words. This method is also applied to low-resource languages such as Tamil by converting Tamil characters to English using transliteration. This is a very challenging novel task for which we provide a dataset of transcripts for the keywords. Despite our model not generalizing well, we achieve a benchmark AP of 0.321 on over 38 words unseen by the model on the MSWC Tamil keyword set. The model also produces an accuracy of 96.2% for classification tasks on the Google Speech Commands dataset. Index Terms: speech recognition, keyword spotting, lowresource languages

Abstract

We leverage the modern advancements in talking head generation to propose an end-to-end system for talking head video compression. Our algorithm transmits pivot frames intermittently while the rest of the talking head video is generated by animating them. We use a state-of-the-art face reenactment network to detect key points in the non-pivot frames and transmit them to the receiver. A dense flow is then calculated to warp a pivot frame to reconstruct the non-pivot ones. Transmitting key points instead of full frames leads to significant compression. We propose a novel algorithm to adaptively select the best-suited pivot frames at regular intervals to provide a smooth experience. We also propose a frame-interpolater at the receiver's end to improve the compression levels further. Finally, a face enhancement network improves reconstruction quality, significantly improving several aspects like the sharpness of the generations. We evaluate our method both qualitatively and quantitatively on benchmark datasets and compare it with multiple compression techniques. We release a demo video and additional information at https://cvit.iiit.ac.in/research/projects/cvit-projects/talking-video-compression.

Abstract

In this paper, we explore an interesting question of what can be obtained from an 8 × 8 pixel video sequence. Surprisingly, it turns out to be quite a lot. We show that when we process this 8 × 8 video with the right set of audio and image priors, we can obtain a full-length, 256 × 256 video. We achieve this 32× scaling of an extremely low-resolution input using our novel audio-visual upsampling network. The audio prior helps to recover the elemental facial details and precise lip shapes and a single high-resolution target identity image prior provides us with rich appearance details. Our approach is an end-to-end multi-stage framework. The first stage produces a coarse intermediate output video that can be then used to animate single target identity image and generate realistic, accurate and high-quality outputs. Our approach is simple and performs exceedingly well (an 8× improvement in FID score) compared to previous super-resolution methods. We also extend our model to talking-face video compression, and show that we obtain a 3.5× improvement in terms of bits/pixel over the previous state-of-the-art. The results from our network are thoroughly analyzed through extensive ablation experiments (in the paper and supplementary material). We also provide the demo video along with code and models on our website

Abstract

In this work, we address the problem of generating speech from silent lip videos for any speaker in the wild. In stark contrast to previous works, our method (i) is not restricted to a fixed number of speakers, (ii) does not explicitly impose constraints on the domain or the vocabulary and (iii) deals with videos that are recorded in the wild as opposed to within laboratory settings. The task presents a host of challenges, with the key one being that many features of the desired target speech, like voice, pitch and linguistic content, cannot be entirely inferred from the silent face video. In order to handle these stochastic variations, we propose a new VAE-GAN

Abstract

Reading, much like music listening, is an immersive experience that transports readers while taking them on an emotional journey. Listening to complementary music has the potential to amplify the reading experience, especially when the music is stylistically cohesive and emotionally relevant. In this paper, we propose the first fully automatic method to build a dense soundtrack for books, which can play high-quality instrumental music for the entirety of the reading duration. Our work employs a unique text processing and music weaving pipeline that determines the context and emotional composition of scenes in a chapter. This allows our method to identify and play relevant excerpts from the soundtrack of the book’s movie adaptation. By relying on the movie composer’s craftsmanship, our book soundtracks include expert-made motifs and other scene-specific musical characteristics. We validate the design decisions of our approach through a perceptual study. Our readers note that the book soundtrack greatly enhanced their reading experience, due to high immersiveness granted via uninterrupted and style-consistent music, and a heightened emotional state attained via high precision emotion and scene context recognition.

Abstract

Texture or repeating patterns, discriminative patches, and shapes are the salient features for various document image analysis problems. This article proposes a deep network architecture that independently learns texture patterns, discriminative patches, and shapes to solve various document image analysis tasks. The considered tasks are document image classifcation, genre identifcation from book covers, scientifc document fgure classifcation, and script identifcation. The presented network learns global, texture, and discriminative features and combines them judicially based on the nature of the problems to be solved. We compare the performance of the proposed approach with state-of-the-art techniques on multiple publicly available datasets such as Book-Cover, rvl-cdip, cvsi and docfigure. Experiments show that our approach outperforms stateof-the-art for the genre and document fgure classifcations and obtains comparable results for document image and script classifcation tasks.