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 3rd Sep'20 and 8th Oct'20
3. are available for duration of 6 months
4. have relevant skills and interests
1. Expertise in key C++ terminologies including vectors, templates, etc.
2. Understanding of pointers and OOPs is a must
3. Proven background in coding competitions like HackerRank
4. Ability to formulate a development problem, design, experiment, and implement solutions in C++
5. Should be self-motivated to fix the issues while coding and look for the solution in open communities like Stackoverflow and Github
6. Good to have experience working with UI development with C++ using tools like QT Creator
7. Must carry their own laptops
Optical character recognition (OCR) is the process of converting the document images into an editable electronic format. This has many advantages like data compression, enabling search or edit options in the images/text, and creating the database for other applications like machine translation, speech recognition, and enhancing dictionaries and language models. 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 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 âError Correction in Indic-OCRâ in our ICDAR-2017 conference paper. We further improve the results with the help of sub-word embeddings in our ICDAR-2019 conference paper. 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 developing an OCR model for the same is our concern. 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).