DUSUNEN Oku 62M — Turkish Printed Word OCR

Name: DUSUNEN-Oku-62M-v1
Availability: InStock

DUSUNEN Oku 62M Turkish OCR benchmark hero

A compact Turkish OCR recognizer for cropped printed-word images, with full support for ç, ğ, ı, İ, ö, ş, ü and measured results on unseen words.

Oku is a 61.6M-parameter TrOCR model. It takes a cropped image containing one printed Turkish word and generates the transcription. Pair it with a text detector when processing full documents.

Try DUSUNEN Oku in the live browser demo

Word-disjoint benchmark

Untouched TrOCR base compared with DUSUNEN Oku

Every vocabulary word occurs in only one dataset split. The 12,500-image test split therefore measures recognition of words unseen during fine-tuning. Both models use identical preprocessing and greedy decoding.

Model	CER ↓	Exact word accuracy ↑
`microsoft/trocr-small-printed`	89.43%	3.44%
DUSUNEN Oku 62M v1	0.24%	98.39%

Relative CER reduction: 99.73%
Case-insensitive exact accuracy: 98.48%
Word error rate: 1.61%
Invalid generated-token rate: 0.0000%

Machine-readable baseline and final results are included in benchmark-baseline.json and benchmark-final.json. reload-validation.json records a second evaluation after loading the saved checkpoint from disk.

Use

python

from PIL import Image
import torch
from transformers import TrOCRProcessor, VisionEncoderDecoderModel

model_id = "GoktugD/DUSUNEN-Oku-62M-v1"
processor = TrOCRProcessor.from_pretrained(model_id)
model = VisionEncoderDecoderModel.from_pretrained(model_id)

image = Image.open("cropped_turkish_word.png").convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
with torch.inference_mode():
    generated = model.generate(pixel_values, max_length=32)
print(processor.batch_decode(generated, skip_special_tokens=True)[0])

Training and reproducibility

Base revision: 04e994ab854b0089d4929f48c2b4dbe2ce78a340
Dataset revision: 79919774c595fcaa0be2feda583fec4cca6f5d2a
Data: 225,000 train / 2,500 validation / 12,500 test
Optimizer: AdamW, linear schedule, 5% warmup, 3e-5
Effective batch size: 32; one epoch; BF16; seed 42
Image size: 384×384; maximum target length: 32 tokens
Hardware: NVIDIA GeForce RTX 5060 Laptop GPU 8 GB
Recorded training time: 2940.4 seconds
Exact training program: train_oku_ocr.py

Scope and limitations

The benchmark images are synthetic. It does not establish accuracy on camera photos, handwriting, scans, receipts, historical print or arbitrary fonts.
Training labels contain Turkish letters only: no digits, punctuation or multi-word lines. Use Oku on cropped word images, not full pages.
OCR output can be wrong. Human review is required for legal, medical, financial or identity documents.
The training dataset is CC BY-SA 4.0. This derivative release preserves that license; see LICENSE for attribution and upstream notes.

Integrity

model.safetensors SHA-256: 601e981bb45c0cf56edd0441097c151c7855a60a738ff8e0d3db493dadacdffe

Developed and released by Göktuğ Düşünen.