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Digitize Text (pre-processing)

As society beecomes more reliant on technology there is an inccrease desire to transition materials that were once hard-copy (e.g., handwritten cursive, handwritten printed and type-writer documents) into electronically saved. The goal of this project is to explore how NLP techniques can assist in cleaning materials after extracting text from images using Optical Character Recognition (OCR) tool, Tesseract. The techniques explored include fine-tuning the pre-trained language model, GPT2.

NOTE: OCR is primarily developed for extracting printed material from images of text.

Code to Run:

Pre-Training Model:

  • File Name: gpt-train.ipynb
  • Change paths to where data is located on local machine
  • Run code blocks in order

Warning: May run into memory issues, so we advise to use Koa's NV-H100 GPU

Prompt Engineering

  • File Name: typed_text_OCR/SmartDoc_OCR_post_processing.ipynb
  • Change paths to where data is located on local machine
  • Run code blocks in order

Evaluating Data

Our primary evaluation metric is cosine similarity

  • File Name: gpt-evaluation-save.ipynb
  • Change paths to where processed data is located on local machine
  • Run code blocks in order

Tesseract

  • File Name: OCR_tesseract_digitize.ipynb
  • Use function run_tesseract_on_images and provide the relative input and output directories
  • For SmartDoc data set run typed_text_OCR/SmartDoc_OCR.ipynb blocks in order

Warning: Second run_tesseract_on_images function concatenates the OCR from multiple images

Data

Structure to access pre-processed data:

  • All data is split and located in dataForModel
  • Within each split of data there is the Betham data and the IAM data, and each folder further has the GT and OCR.
  • Train: Val: Test == 70:20:10

Important to Note for Raw Data Pre-Processing:

IAM Database

  • Forms => images of whole pieces of paper, GT data is located on the forms
  • used lines to extract the handwriting from files and concatenated to be one line in .txt
  • GT was located in xml files based on position, and extracted as one line in .txt file

Bentham Dataset

  • GT in XML files so extracted as one line in .txt
  • Pages has whole handwritten no GT on top. There is \n throughout OCR

SmartDoc dataset

Uncommon Imports

Importing Google's PyTesseract

  • use brew install tesseract
  • to find executable which tesseract

Authors

  • Joel Nicolow
  • Amanda Nitta
  • Jan (Mark) Schittenhelm