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Serovvans/
trocr-prereform-orthography

Name: trocr-prereform-orthography
Availability: InStock

safetensors

vision-encoder-decoder

openrail

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trocr-old-russian

Info

The model is trained to recognize printed texts in Old Russian language

Use microsoft/trocr-small-printed as base model for fine-tune.
Fine-tune on 636k text images from dataset: https://huggingface.co/datasets/nevmenandr/russian-old-orthography-ocr

Usage

Base-usage

python

from PIL import Image
from transformers import TrOCRProcessor, VisionEncoderDecoderModel

hf_model = VisionEncoderDecoderModel.from_pretrained("Serovvans/trocr-prereform-orthography")

image = Image.open("./path/to/your/image")

processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
pixel_values = processor(images=image, return_tensors="pt").pixel_values

generated_ids = hf_model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)

Usage for recognizing the page

Download the following files from the repository: recognize_page.py, requirements_page.txt
Install tesseract-ocr
- Linux
sudo apt install tesseract-ocr
- MacOS
brew install tesseract-ocr
- Windows
  1. Download tesseract exe from https://github.com/UB-Mannheim/tesseract/wiki.
  2. Install this exe in C:\Program Files (x86)\Tesseract-OCR
  3. Open virtual machine command prompt in windows or anaconda prompt.
Run this command
- Linux
sudo apt-get install poppler-utils
- MacOS
brew install poppler
- Windows
  1. Download the latest poppler package from @oschwartz10612 version which is the most up-to-date.
  2. Move the extracted directory to the desired place on your system
  3. Add the bin/ directory to your PATH
  4. Test that all went well by opening cmd and making sure that you can call pdftoppm -h
Install dependencies:

pip install -r requirements_page.txt

Use this code

python

from recognize_page import recognize_page

page_path = "cleaned_pages/C2V10/page11.png"
text = recognize_page(page_path, text_output_path="output/file.txt")

print(f"Текст страницы:\n")
print(text)

Usage for recognizing the book from pdf-file

Download the following files from the repository: recognize_book.py, recognize_page.py, requirements_book.txt
Install tesseract-ocr
- Linux
sudo apt install tesseract-ocr
- MacOS
brew install tesseract-ocr
- Windows
  1. Download tesseract exe from https://github.com/UB-Mannheim/tesseract/wiki.
  2. Install this exe in C:\Program Files (x86)\Tesseract-OCR
  3. Open virtual machine command prompt in windows or anaconda prompt.
Run this command
- Linux
sudo apt-get install poppler-utils
- MacOS
brew install poppler
- Windows
  1. Download the latest poppler package from @oschwartz10612 version which is the most up-to-date.
  2. Move the extracted directory to the desired place on your system
  3. Add the bin/ directory to your PATH
  4. Test that all went well by opening cmd and making sure that you can call pdftoppm -h
Install dependencies:

pip install -r requirements_book.txt

Use this code

python

from recognize_book import recognize_book

recognize_book(book_path="path/to/your/book.pdf", text_output_path="book_text.txt")

Metrics on test

CER (Char Error Rate) = 0.095
WER (Word Error Rate) = 0.298