Only those candidates can apply who:
1. are available for the work from home job/internship
2. can start the work from home job/internship between 2nd May'21 and 6th Jun'21
3. are available for duration of 6 months
4. have relevant skills and interests
We are working on detecting the layout of the OCRed text, preserving it, and reflecting it to automate the annotation task. Optical character recognition (OCR) is the process of converting document images into an editable electronic format. OCR in Indian languages is quite challenging due to richness in inflections. Using open-source and commercial OCR systems, we have observed the word error rates (WER) of around 20-50% on printed documents in four different Indic languages. Moreover, developing a highly accurate OCR system with an accuracy as high as 90% is not useful unless aided by the mechanism to identify errors. So, we started with the problem of developing "OpenOCRCorrect", an end-to-end framework for error detection and corrections in Indic-OCR. Our models outperform state-of-the-art results in 'Error Detection in Indic-OCR' for six Indic languages with varied inflections and we have solved the out of vocabulary problem for “Error Correction in Indic-OCR” in our ICDAR-2017 conference paper. We further improve the results with the help of sub-word embeddings in our ICDAR-2019 conference paper.
There is an immediate demand to keep the softcopy of the Indian preserved texts. Currently, we are targeting Sanskrit. Although the OCR tools available online do a decent job on English texts, they are not optimized for Indic languages. Thus, we developed an in-house OCR model for the same. The model can detect text with the maximum level of accuracy and can draw bounding boxes on each line of the text. Further, in the digitization process of such texts, the second step would be spelling correction and formatting of the text detected by the OCR models. The demo video for our framework is available here: https://www.youtube.com/watch?v=u9bqUDrGugc. To install the software, you can go to https://github.com/rohitsaluja22/OpenOCRCorrect and follow the instructions given in https://www.youtube.com/watch?v=0hcdlF-zn8E.
Our team 'CLAM' secured 2nd position in the multilingual PostOCR competition at ICDAR'19. Our model achieved the highest corrections of 44% in Finnish, which is significantly higher than the overall topper (8% in Finnish). Our final report for the same can be checked here: https://drive.google.com/file/d/1uuBWu1LQ1QZ49SCgLBoB1er4HpWSzmcx/view. The source code for our framework is available here: https://github.com/rohitsaluja22/OpenOCRCorrect
You can read our paper here:
1. ICDAR 2019: https://www.cse.iitb.ac.in/~rohitsaluja/PID6011473.pdf
2. ICDAR 2017: https://ieeexplore.ieee.org/document/8269944
3. ICDAR-OST 2017: https://ieeexplore.ieee.org/abstract/document/8270254