Cronosteak/ocr-corrector-flan-t5-base

OCR Corrector — flan-t5-base

Fine-tuned `google/flan-t5-base` (250M parameters) that corrects OCR errors in electrical engineering academic text.

This is the model released with the paper "Automatic OCR Error Correction in Electrical Engineering Academic Papers Using Sequence-to-Sequence Models" (IEEE LA-CCI 2026).

• —Code and dataset: https://github.com/Cronosteak/ocr-corrector-papers
• —License: MIT

Usage

The model expects the prefix correct OCR: on every input.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "Cronosteak/ocr-corrector-flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

text = "correct OCR: Thc powcr systcm opcrates at 60 Hz"
inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(**inputs, max_new_tokens=256, num_beams=4)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training data

6,440 synthetic OCR–ground-truth pairs built without manual annotation: open-access electrical engineering abstracts were collected from the OpenAlex API, then corrupted with synthetic OCR noise (character confusions, spurious spaces/hyphens, deleted spaces, duplicated characters) at four rates: r ∈ {0.02, 0.04, 0.06, 0.08}.

Splits are per-document (80/10/10) to avoid leakage between train and test.

Results

Held-out test set of 676 pairs:

A 37.5% relative CER reduction and an 80.9% relative WER reduction over the uncorrected input. Per-noise-rate metrics are in eval_results.json.

Ablations showed that multi-level noise diversity is an important contributor to generalization: restricting training to r=0.04 alone drops exact match from 44.23% to 35.21% (that variant also trains on fewer pairs, so diversity and dataset size are not fully disentangled).

Training procedure

Limitations

• —Trained on synthetic OCR noise, not on real Tesseract output. Real-world error distributions may differ.
• —Domain is electrical engineering and adjacent fields (computer science, engineering) in English; performance on other domains or languages is untested.
• —Residual errors concentrate on lowercase common words at the highest noise rate (r=0.08), where corrupted word shapes can be decoded into plausible but wrong alternatives. Because it is a generative model, it can rewrite text that was already correct — review output before using it in a critical pipeline.
• —Inputs are truncated at 256 tokens; long passages must be chunked.

Citation

@inproceedings{espinosa2026ocr,
  title     = {Automatic OCR Error Correction in Electrical Engineering
               Academic Papers Using Sequence-to-Sequence Models},
  author    = {Espinosa Ortiz, Breinner Farid and
               Forero Mendoza, Leonardo Alfredo and
               Pacheco, Marco Aur{\'e}lio C. and
               Batista, Evelyn C. S.},
  booktitle = {2026 IEEE Latin American Conference on Computational
               Intelligence (LA-CCI)},
  year      = {2026},
  publisher = {IEEE}
}