Towards Making Flowchart Images Machine Interpretable

1. Easy OCR (2022). https://pypi.org/project/easyocr/1.6.2/

2. Ahmad, W., Chakraborty, S., Ray, B., Chang, K.W.: Unified pre-training for program understanding and generation. In: Proceedings of NAACL-HLT (2021)

   Google Scholar 

3. Akermi, I., Heinecke, J., Herledan, F.: Transformer based natural language generation for question-answering. In: Proceedings of the 13th International Conference on Natural Language Generation (2020)

   Google Scholar 

4. Austin, J., et al.: Program synthesis with large language models. CoRR abs/2108.07732 (2021)

   Google Scholar 

5. Awal, A.M., Feng, G., Mouchère, H., Viard-Gaudin, C.: Handwritten flowchart dataset (OHFCD). Document Recognition and Retrieval XVIII, January 2011, San Fransisco, United States, pp. 7874–78740A (2011). https://doi.org/10.1117/12.876624

6. Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: Proceedings of CVPR (2019)

   Google Scholar 

7. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 120, 122–125 (2000)

   Google Scholar 

8. Brown, T., et al.: Language models are few-shot learners. In: Proceedings of NeurIPS (2020)

   Google Scholar 

9. Carton, C., Lemaitre, A., Coüasnon, B.: Fusion of statistical and structural information for flowchart recognition. In: Proceedings of ICDAR (2013)

   Google Scholar 

10. Chaudhuri, A.: Flowchart and Algorithm Basics: The Art of Programming. Mercury Learning and Information (2020)

    Google Scholar 

11. Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. In: Proceedings of ICLR (2020)

    Google Scholar 

12. Cook, D.: Flowgorithm (2022). https://www.flowgorithm.org/

13. Fang, J., Feng, Z., Cai, B.: DrawnNet: offline hand-drawn diagram recognition based on keypoint prediction of aggregating geometric characteristics. Entropy 24(3), 425 (2022)

    Article  Google Scholar 

14. Feng, Z., et al.: CodeBERT: a pre-trained model for programming and natural languages. In: Findings of the ACL: EMNLP (2020)

    Google Scholar 

15. Guo, D., et al.: GraphCodeBERT: pre-training code representations with data flow. In: Proceedings of ICLR (2020)

    Google Scholar 

16. Herrera-Camara, J.I., Hammond, T.: Flow2Code: from hand-drawn flowcharts to code execution. In: Proceedings of the Symposium on Sketch-Based Interfaces and Modeling (2017)

    Google Scholar 

17. Kenton, J.D.M.W.C., Toutanova, L.K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT (2019)

    Google Scholar 

18. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) Proceedings of ICLR (2015)

    Google Scholar 

19. Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of ACL (2020)

    Google Scholar 

20. Li, M., et al.: TrOCR: transformer-based optical character recognition with pre-trained models. arXiv preprint arXiv:2109.10282 (2021)

21. Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. CoRR abs/1907.11692 (2019)

    Google Scholar 

22. Lu, S., et al.: CodeXGLUE: a machine learning benchmark dataset for code understanding and generation. arXiv (2021)

    Google Scholar 

23. Montellano, C.D.B., Garcia, C.O.F.C., Leija, R.O.C.: Recognition of handwritten flowcharts using convolutional neural networks. Int. J. Comput. Appl. (2022)

    Google Scholar 

24. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of ACL (2002)

    Google Scholar 

25. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training. OpenAI (2018)

    Google Scholar 

26. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 1–67 (2020)

    MathSciNet  MATH  Google Scholar 

27. Ren, S., et al.: CodeBLEU: a method for automatic evaluation of code synthesis. CoRR abs/2009.10297 (2020)

    Google Scholar 

28. Schäfer, B., Stuckenschmidt, H.: Arrow R-CNN for flowchart recognition. In: Proceedings of ICDAR Workshop (2019)

    Google Scholar 

29. Song, K., Tan, X., Qin, T., Lu, J., Liu, T.Y.: MASS: masked sequence to sequence pre-training for language generation. In: Proceedings of ICML (2019)

    Google Scholar 

30. Supaartagorn, C.: Web application for automatic code generator using a structured flowchart. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) (2017)

    Google Scholar 

31. Tannert, S., Feighelstein, M., Bogojeska, J., Shtok, J., Staar, A.A.P., Karlinsky, A.S.J.K.L.: FlowchartQA: the first large-scale benchmark for reasoning over flowcharts. In: Document Intelligence Workshop @ KDD (2022)

    Google Scholar 

32. Vaswani, A., et al.: Attention is all you need. In: Proceedings of NeurIPS (2017)

    Google Scholar 

33. Wang, Y., Wang, W., Joty, S., Hoi, S.C.: CodeT5: identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In: Proceedings of EMNLP (2021)

    Google Scholar 

34. Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of EMNLP: System Demonstrations (2020)

    Google Scholar 

35. Wu, X.H., Qu, M.C., Liu, Z.Q., Li, J.Z., et al.: Research and application of code automatic generation algorithm based on structured flowchart. J. Softw. Eng. Appl. 4, 534–545 (2011)

    Article  Google Scholar 

Download references