m-mjm/trocr-finetuned-typewritten

TrOCR Fine-tuned for Typewritten Text

A fine-tuned version of microsoft/trocr-base-printed optimized for typewritten historical documents, specifically U.S. County deed record indices.

Model Description

• —Base Model: microsoft/trocr-base-printed
• —License: MIT
• —Task: Optical Character Recognition (OCR) for typewritten text

This model was fine-tuned on 8,100 manually annotated images of typewritten index entries from historical deed records. It achieves near-perfect accuracy on this domain, with an exact match rate of 99.88%.

Intended Use

Primary use case: OCR for typewritten documents, particularly historical records with similar formatting to mid-20th century U.S. county indices.

Limitations: This model was trained on a specific document style. Performance on handwritten text, modern fonts, or significantly different layouts has not been evaluated and may be poor.

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Usage

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

processor = TrOCRProcessor.from_pretrained("m-mjm/trocr-finetuned-typewritten")
model = VisionEncoderDecoderModel.from_pretrained("m-mjm/trocr-finetuned-typewritten")

# Load image (ensure this is a crop of a text line, not a full page)
image = Image.open("your_image.ext").convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values, num_beams=4)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_text)

Training Details

Data Preparation

The training dataset was created from high-resolution scans of typewritten deed record indices. Each entry was segmented and manually transcribed to create ground truth labels.

To prevent overfitting, two augmentation strategies were applied:

1. 1.Column reordering: Text segments were cropped and recombined in different arrangements
2. 2.Noise augmentation: Applied the same noise transformations used in the original TrOCR training (see noise.py in the TrOCR repository)

Hyperparameters

Dataset Split

• —Training samples: 7,315
• —Evaluation samples: 813

Performance

Exact match was used as the primary training metric. Standard CER/WER can appear low while still producing unacceptable results for structured data extraction, so exact match provided a more reliable signal during training.

Repository

Training notebook and data preparation scripts: GitHub

Citation

If you use this model, please cite the original TrOCR paper:

@misc{li2021trocr,
  title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models},
  author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei},
  year={2021},
  eprint={2109.10282},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}