Changes

Jump to navigation Jump to search
Line 55: Line 55:     
Each approach requires different kinds of annotation, so chosing the most effective approach at the beginning of the project is crucial.
 
Each approach requires different kinds of annotation, so chosing the most effective approach at the beginning of the project is crucial.
 +
 +
[[File:Nutrition Facts extraction approaches (1).png]]
 +
 +
Source: https://docs.google.com/drawings/d/1YKgnTEX1RBgsMQpO4JIt94sMwf1IC_fPWIvPnUjswTg/edit?usp=sharing
 +
 +
= Planning =
 +
 +
* Test of an end-to-end model, through an API as a starter: Form Recognizer or Google Document AI API. If results are promising, we develop in-house end-to-end model, otherwise a layout model.
 +
* Thorough literature review
 +
* Architecture choice
 +
* Annotation campaign
 +
* Experimentation: model training and scoring
 +
* Integration to Robotoff/Hunger Games
 +
 +
Ramzi: test of end-to-end model with Form Recognizer API.
 +
 +
Yichen: test of image preprocessing before Form Recognizer (layout model), test "manual" approaches.
 +
* tested a pre-processing method (ported from Mathematica to python)
 +
* OCR results worsen after binarization
 +
* testing method to morph deformed images (by detecting horizontal and vertical lines)
 +
* "manual" approaches: compare positions of bounding boxes of OCR, use borders if we have some
 +
** manual horizontal or vertical scans of bounding boxes, using the angles of bounding boxes
 +
** robotoff has python classes for importing/analyzing Google Cloud Vision
 +
 +
Raphaël: Literature review (to be continued)
 +
 +
= Notes 24/07 =
 +
 +
* Ramzi: tested end-to-end model with learning
 +
** missing one step, results expected next week
 +
* Yasmine + Yichen: working on pre-processing
 +
** Testing cylinders distortions -> flat rectangle
 +
*** open cv
 +
*** Tesseract performance very poor
 +
 +
Next week:
 +
* Ramzi
 +
** finish test end-to-end model
 +
** increase size of data set to get significant results
 +
* Yasmine + Yichen
 +
** Cylinder unwrapping (classic + deep learning models)
 +
*** review litterature
 +
*** create data set for Ramzi using paid API "perfect label"
 +
** table structure detection : associate bounding boxes to key value pairs
 +
* All
 +
** Create test set with cylindric photos + unwrapped photos
 +
 +
= Resources =
 +
 +
* [https://aka.ms/trove Trove] - marketplace platform where AI developers who need photos can create projects looking for specific types of photos crowdsource photos from photo takers.
 +
* Lobe - train a custom machine learning model using a simple visual interface with no code
 +
** Step 1: Request an invite code from the team: lobeai@microsoft.com
 +
** Step 2: Download Lobe - For Mac: https://aka.ms/DownloadLobeMac - For PC: https://aka.ms/DownloadLobeWindows
 +
* [https://flow.microsoft.com/en-us/ai-builder/ AI Builder] - import your photos right away into a Power App (or a Power Automate flow) so you can use the output in any real world application.

Navigation menu