Code Runner: Solution for Recognition and Execution of ...

[Pages:2]Code Runner: Solution for Recognition and Execution of Handwritten Code

Wenxiao Du wxdu@stanford.edu

Motivation In technical interviews, interviewees are sometimes asked to write code on white board or paper. Interviewer can see the general logic of the code but cannot actually run it and prove its correctness. Therefore, the goal of my project is to provide a solution that would allow people take a picture of a piece of code by android phone and get its output. For now the target code is python. More programming languages will be included if time allows.

Implementation details The entire project would follow the client-server model. Therefore, the server side can do more complex computation and involve more programming language. Here is the detail of workflow: 1. Client (Android Phone) takes a picture and sends it to server 2. Preprocessing the image

In this step, size normalization is first conducted on image. Then I would denoise the image use median filter and remove the small regions, which should be dust or noise. After that, the image is binarized using Otsu Method. 3. Recognize the code I would use Tesseract-OCR engine in this Part. Tesseract is originally designed for printed text recognition, but after proper training, it can also be used for handwritten text recognition.[1][2][3][6] Some datasets have already been collected for training purpose.[4] 4. Post-processing the result Since there should be some common key words for a certain programming language e.g. `def' in Python, I would build a dictionary contains such key words and run the spell corrector on each recognized term. The general idea is that I would search for all

candidate words that are within the certain edit distance from the given word and see whether any candidates match words in dictionary. [5] 5. The result is then sent back to the client. Client can now manually check whether there is still any mis-recognition. After that the client can compile the final-version code and produce the output. For this part, I haven't decided whether let server or client to do the compilation. The decision would be made when we come to this step.

References: [1] Training Tesseract 3 Code.. Google, June 2013. Web. 06 June 2013 [2] Recognition of Handwritten Textual Annotations using Tesseract Open Source OCR Engine for information Just In Time (iJIT), Sandip Rakshit, Subhadip Basu, Hisashi Ikeda 03/2010; [3] Recognition of Handwritten Roman Script Using Tesseract Open source OCR Engine Sandip Rakshit, Subhadip Basu 03/2010;

[4] IAM Handwriting Database Sam Roweis' Dataset UJI Pen Characters Data Set

[5] How to Write a Spelling Corrector. Peter Norvig. [6] "An Overview of the Tesseract OCR Engine." Smith, Ray. Research at Google. Google, 2007. Web. 6 June 2013.

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