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 25th Nov'20 and 30th Dec'20
3. are available for duration of 6 months
4. have relevant skills and interests
1. Expertise in key Python and C++ terminologies including data structures, vectors, templates, etc.
2. A good understanding of backend and MVC architecture
3. Understanding of pointers and OOPs is a must
4. Proven background in coding competitions like HackerRank
5. Ability to formulate a development problem, design, experiment, and implement solutions in C++ and Python
6. Should be self-motivated to fix the issues while coding and look for the solution in open communities like Stackoverflow and Github
7. Good to have experience working with application development with C++ using tools like QT Creator or in Python using the Django framework
8. Must carry their own laptops
Optical character recognition (OCR) is the process of converting the document images into an editable electronic format. 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. The demo video for our framework is https://www.youtube.com/watch?v=u9bqUDrGugc. The model should be able to detect text with maximum level accuracy and should be able to 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.
'ICDAR 2019 Post-OCR competition': 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).
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