Punjabi Text Recognition System for Portable Devices: A Comparative Performance Analysis of Cloud Vision API with Tesseract

Ravneet Kaur(1), Dharam Veer Sharma(2),


(1) DAV College, Jalandhar
(2) Punjabi University, Patiala

Abstract


The increasing availability of high performance, low priced, portable digital imaging devices has created an opportunity for on demand analysis of documents. In this paper, Punjabi Text Recognition System is developed for portable devices using two different approaches that is Google’s Cloud Vision APIs and LSTM based Tesseract OCR Engine. The performance of developed mobile based systems is compared in term of runtime and recognition accuracy. Both Vision API and LSTM based OCR engine provides good results for Roman Based Scripts. Particularly for Gurmukhi text document images, Cloud Vision API recognizes Punjabi with good accuracy as compared to Tesseract. We presented a detailed comparison and computed the character and word level accuracy of both the systems for same set of images.


Keywords


Mobile OCR; Gurumukhi OCR; Character Recognition; Tesseract; Vision API

Full Text:

PDF

References


S Adepu and R F Adler, “A Comparison of Performance and Preference on Mobile Devices vs. Desktop Computers,” IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 2016, pp. 1-7, doi: 10.1109/UEMCON.2016.7777808.

C Bröhl, P Rasche, J Jablonski, S Theis, M Wille and A Mertensr, “Desktop PC, Tablet PC, or Smartphone? An Analysis of Use Preferences in Daily Activities for Different Technology Generations of a Worldwide Sample,” 2018, 10.1007/978-3-319-92034-4_1.

G S Lehal and C Singh, “A Gurmukhi Script Recognition System,” Proceeding’s 15th International Conference on Pattern Recognition. ICPR-2000, Vol. 2, pp. 557–560.

G Chang, C Tan, G Li, and C Zhu, ”Developing Mobile Applications on the Android Platform,” Springer Berlin Heidelberg, 2010, pp. 264-286.

Liu Jianye, Yu Jiankun, “Research on Development of Android Applications,” Fourth International Conference on Intelligent Networks and Intelligent Systems, IEEE Computer Society, 2011, pp. 69-72.

Suhas Holla, Mahima M Katti, “Android Based Mobile Application Development and Its Security,” International Journal of Computer Trends and Technology- ISSN: 2231-2803 Vol.3, No. 3, pp. 486-490, 2012.

Li Ma, Lei Gu, Jin Wang, “Research and Development of Mobile application for Android Platform,” International Journal of Multimedia and Ubiquitous Engineering (IJMUE) Vol. 9, No. 4, pp.187-198, 2014.

Kirthika, Prabhu, Visalakshi, “Android Operating System: A Review,” International Journal of Trend in Research and Development (IJTRD), ISSN 2394-9333, Vol. 2, No. 5, pp 260-264, 2015.

Chen, Shih-Hsin & Chen, Yi-Hui, “A Content-Based Image Retrieval Method Based on the Google Cloud Vision API and WordNet,” pp. 651-662, 2017. 10.1007/978-3-319-54472-4_61.

D N Fariz, T L Emha, “Implementation of Real-Time Scanner Java Language Text with Mobile Vision Android Based,” IEEE International Conference on Information and Communications Technology (ICOIACT), pp. 724-729, 2018.

DAS –Short Papers Booklet, 13th IAPR International Workshop on Document Analysis Systems, pp. 1-28, 2018

Hsiu-Wei Yang, Linqing Liu, Ian Milligan, Nick Ruest and Jimmy Lin, “Scalable Content-Based Analysis of Images in Web Archives with TensorFlow and the Archives Unleashed Toolkit,” ACM/IEEE Joint Conference on Digital Libraries, JCDL- 2019, pp 436-437.

D S Kurniawan, A R Della, S Karina, S Dewi, P Yudy, “Mobile Financial Management Application using Google Cloud Vision API,” 4th International Conference on Computer Science and Computational Intelligence (ICCSCI), 12-13 September 2019, pp 596-604.

B Allessio and H Valeria, “Camera Keyboard: A Novel Interaction Technique for Text Entry Through Smartphone Cameras,” IEEE Access, Vol 7, pp. 167982-996, 2019.

R Smith, “An overview of the Tesseract OCR Engine,” 9th International Conference on Document Analysis and Recognition (ICDAR 2007) IEEE, Curitiba, Brazil, pp. 629-633, 2007.

S Badla, “Improving the efficiency of Tesseract OCR Engine,” Master's projects, 2014, https://doi.org/10.31979/etd.5avd-kf2g https://scholarworks.sjsu.edu/etd_projects/420.

G A Robby, A Tandra, I Susanto, J Harefa, A Chowanda, “Implementation of Optical Character Recognition using Tesseract with the Javanese Script Target in Android Application. Procedia Computer Science, Volume 157, Pages 499-505, ISSN 1877-0509, 2019. https://doi.org/10.1016/j.procs.2019.09.006.

N Pawar, Z Shaikh, P Shinde, Y Warke, “Image to text conversion using tesseract,” International Research Journal of Engineering and Technology, 6(2): 516-519, 2019.

Misha Iakovlev, “The Battle of the OCR Engines: Tesseract vs Google Vision,“ Fuzzy Labs. https://fuzzylabs.ai/blog/the-battle-of-the-ocr-engines/. Accessed on 05 April, 2021.


Refbacks

  • There are currently no refbacks.


Journal of Computer Science and Engineering (JCSE)
ISSN 2721-0251 (online)
Published by : ICSE (Institute of Computer Sciences and Engineering)
Website : http://icsejournal.com/index.php/JCSE/
Email: jcse@icsejournal.com

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.