Improving Word Recognition using Multiple Hypotheses and Deep Embeddings

Siddhant Bansal   Praveen Krishnan   C.V. Jawahar  

ICPR 2020

We propose to fuse recognition-based and recognition-free approaches for word recognition using learning-based methods. For this purpose, results obtained using a text recognizer and deep embeddings (generated using an End2End network) are fused. To further improve the embeddings, we propose EmbedNet, it uses triplet loss for training and learns an embedding space where the embedding of the word image lies closer to its corresponding text transcription’s embedding. This updated embedding space helps in choosing the correct prediction with higher confidence. To further improve the accuracy, we propose a plug-and-play module called Confidence based Accuracy Booster (CAB). It takes in the confidence scores obtained from the text recognizer and Euclidean distances between the embeddings and generates an updated distance vector. This vector has lower distance values for the correct words and higher distance values for the incorrect words. We rigorously evaluate our proposed method systematically on a collection of books that are in the Hindi language. Our method achieves an absolute improvement of around 10% in terms of word recognition accuracy.

For generating the textual transcription, we pass the word image through the CRNN and the End2End network (E2E), simultaneously. The CRNN generates multiple (K) textual transcriptions for the input image, whereas the E2E network generates the word image's embedding. The K textual transcriptions generated by the CRNN are passed through the E2E network to generate their embeddings. We pass these embeddings through the EmbedNet proposed in this work. The EmbedNet projects the input embedding to an updated Euclidean space, using which we get updated word image embedding and K transcriptions' embedding. We calculate the Euclidean distance between the input embedding and each of the K textual transcriptions. We then pass the distance values through the novel Confidence based Accuracy Booster (CAB), which uses them and the confidence scores from the CRNN to generate an updated list of Euclidean distance, which helps in selecting the correct prediction.

Paper

• ArXiv: PDF
• ICPR: Coming soon!

 
@misc{bansal2020improving,
      title={Improving Word Recognition using Multiple Hypotheses and Deep Embeddings}, 
      author={Siddhant Bansal and Praveen Krishnan and C. V. Jawahar},
      year={2020},
      eprint={2010.14411},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Code

This work is implemented using the pytorch neural network framework. Code is available in this GitHub repository: .

DGAZE Dataset for driver gaze mapping on road

Isha Dua   Thrupthi John   Riya Gupta   C.V. Jawahar

Mercedes Benz   IIIT Hyderabad   IIIT Hyderabad   IIIT Hyderabad

IROS 2020

Dataset Overview

Use Cases

Citation

All documents and papers that report research results obtained using the DGAZE dataset should cite the below paper:
Citation: Isha Dua, Thrupthi Ann John, Riya Gupta and C. V. Jawahar. DGAZE:Diver Gaze Mapping On Road In IROS 2020.

Bibtex

 @inproceedings{isha2020iros, 

          title={DGAZE: Driver Gaze Mapping on Road}, 

          author={Dua, Isha and John, Thrupthi Ann and Gupta, Riya and Jawahar, CV}, 

          booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020}, 

          year={2020} 

}

Download Dataset

The DGAZE dataset is available free of charge for research purposes only. Please use the link below to download the dataset (password protected zip file). We request you to send a mail to Thrupthi Ann John and Isha Dua (contact details below) with the duly filled dataset agreement form, upon which we will send you the password. .
Disclaimer: While every effort has been made to ensure accuracy, DGaze database owners cannot accept responsibility for errors or omissions.
[Download Dataset Agreement ] [Download Dataset] [Download README]

Acknowledgements

We would like to thank Akshay Uttama Nambi and Venkat Padmanabhan from Microsoft Research for providing us with resources to collect DGAZE dataset. This work is partly supported by DST through the IMPRINT program. Thrupthi Ann John is supported by Visvesvaraya Ph.D. fellowship.

Contact

For any queries about the dataset, please contact the authors below:
Isha Dua: This email address is being protected from spambots. You need JavaScript enabled to view it.
Thrupthi Ann John: This email address is being protected from spambots. You need JavaScript enabled to view it.

Retrieval from Large Document Image Collections

Overview

Extracting the relevant information out of a large number of documents is quite a challenging and tedious task.The quality of results generated by the traditionally available full-text search engine and text-based image retrieval systems is not very optimal. These Information Retrieval(IR) tasks become more challenging with the non-traditional language scripts for example in the case of Indic scripts. We have developed OCR (Optical Character Recognition) Search Engine which is an attempt to make an Information Retrieval and Extraction (IRE) system that replicates the current state-of-the-art methods using the IRE and basic Natural Language Processing (NLP) techniques. In this project we have tried to demonstrate the study of the methods that are being used for performing search and retrieval tasks. We also present the small descriptions of the functionalities supported in our system along with the statistics of the dataset. We use Indic-OCR developed at CVIT, for generating the text for the OCR Search Engine.

This is the basic pipeline followed by the OCR Search Engine. Digitised images after the preprocessing steps, ex. denoising, are passed through a segmentation pipeline, which generates line level segmentations for the document. These together are passed in the Indic OCR developed at CVIT that generates the text output . This text output, segmented output (line level segmentations) and document images form the database of the IRE system.

Functionalities implemented in the IRE system

The developed IRE system performs the following functionalities on the user end:

• Search is based on the keywords entered by the users in this case i.e whether they are single or multiple word query or the query with double quotes.
• Search is based on the language chosen. It also supports multilingual search which enables users to search the books with multiple languages, for instance in the case of "Bhagawadgitha" where a shlokaand its translation is provided.
• Search is based on how relevant the search results are and provide the users with the highlighted line/lines containing searched queries within the complete text.
• Additionally, primary (language, type, author, etc) and secondary(genre and source) filters for the books have been added to ease up the task of search.
• Our system also supports transliteration which enables users to take the benefit of the phonetics of the other language while typing.

Live Demo

--- We will update the link ---

Code available on : [Code]

Dataset

The datasets (NDLI data & British library data) contains document images of the digitised books in the Indic languages

The dataset provided for use consists of more than 1.5 million documents in languages Hindi, Telugu and Tamil (~ 5 lacs each). This dataset consists of the books from various genres such as religious texts, historical texts, science and philosophy. Dataset statistics are presented in the table given below.

Statistics of the NDLI dataset and the British Library dataset.

In addition to the existing Indic languages, we are also working on the Bangla data which is provided to us by the British Library. Our OCR model is trying to improve the text predictions for the same which will help in achieving better search results.

Acknowledgements

This work was supported, in part, by the National Digital Library of India (IIT Kharagpur) who provided us with more than 1.5 million document images in Indian languages (Hindi, Tamil and Telugu). We also thanks British Library for providing us with the ancient document images in Bangla language. I would also like to acknowledge Krishna Tulsyan for assisting the project, initially.

Contact

For further information about this project please feel free to contact :
Riya Gupta - This email address is being protected from spambots. You need JavaScript enabled to view it.
Dataset and Server Management: Varun Bhargavan, Aradhana Vinod [varun.bhargavan;aradhana.vinod]@research.iiit.ac.in

Indiscapes: Instance segmentation networks for layout parsing of historical indic manuscripts

Abstract

To access the code and paper click here