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This project aims to speed up data collection for mathematical equation detection and OCR by employing a CNN to eliminate square grids from handwritten notes, thus generating clean images for efficient text extraction.

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Handwritten-notes Denoising

This project provides tools to accelerate the collection of data needed to address mathematical equation detection and optical character recognition (OCR) in handwritten notes. Handwritten notes pose several difficulties for automatic extraction of text and equations, mainly because of the inherent noise created when notes are taken on lined or squared sheets, rather than on a blank sheet of paper. Our goal is to have a Convolutional Neural Network (CNN) trained to remove square grids from handwritten notes and to obtain a text-only, noise-free image. The work was developed as part of the "Computer Vision and cognitive systems" examination at the University of Modena and Reggio Emilia.

Paper: Handwritten Notes Denoising

Presentation: Handwritten Notes Denoising

Aknowledgements

AUTHORs CONTACTs GITHUBs
Olmo Baldoni [email protected] olmobaldoni
Cristian Bellucci [email protected] cleb98
Danilo Caputo [email protected] RiccardoSanti092

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MIT

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This project aims to speed up data collection for mathematical equation detection and OCR by employing a CNN to eliminate square grids from handwritten notes, thus generating clean images for efficient text extraction.

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  • Python 64.9%
  • Jupyter Notebook 33.7%
  • Shell 1.4%