/EECS442_CV_Proj

This repo is the final proj for EECS442 Computer Vision in Umich F18 titled Document Reconstruction and Contextualization.

Primary LanguageMATLABMIT LicenseMIT

EECS442_CV_Proj

This repo is the final proj for EECS442 Computer Vision in Umich F18 titled Reconstruction and Textualization of Shredded Paper Documents by Feature Matching.


Run & Test:

  1. run run_lengthwaycut on the cmd of matlab, this will provide the result on the lengthway cut approach, which will generate corresponding files on the folder.
  2. run run_gridcut on the cmd of matlab, this will provide the result on the grid cut approach, which will generate corresponding files on the folder.

Timeline of our work (check with our commit history for more):

2017/12/02:

Step0: split image in nonrandom size mode (done) and randome size mode (TBA) Step1: vertical bar reconstruction (done) Step2: laterial bar reconstruction (TBA)

2017/12/03:

  1. fix the bidirection problem in vertical bar reconstruction bug. Now just start from left side.
  2. go through the cube_cut case. Finished it in full_text case.
  3. Next stage is to improve the performance of reconstruction. And start contextualization (i.e. ocr).

2017/12/04:

  1. Add and test the matlab ocr and text saving. Finish the example. Next step is to make it a general case.
  2. TODO: improve the cubecut performance and prevent mismatching dimension error in programming.

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2017/12/05:

  1. Submit your project report and the code as separate files, e.g., one pdf and one zip file. If your group created a GitHub repo, you could include the link in your report (but you still need to upload code here).

The report should have:

an introduction section, (documentation reconstruction significant + context) ----> dzhang

a related work section, (iteration: vertical, cube, ocr) ---> pwl

a methods section, ( method:vertical->similarity, cube->space indent, ocr->matlab build-in function ) ---> dzhang

a results section ( vertical -> niubi, [3,5] niubi, space is big-> cube niubi, space is small-> cube beng ) -----> pwl

a discussion (linear scoring, limit) ---> dzhang .

Grammer check and refine: YY & zhaoyic