---
library_name: transformers
pipeline_tag: image-to-text
base_model: microsoft/trocr-base-printed
tags:
- trocr
- vision-encoder-decoder
- image-to-text
- ocr
- captcha
- math-captcha
- synthetic-data
---

# OCR Match Captcha

A TrOCR model fine-tuned to recognize short mathematical captcha
expressions such as `26+7=?` and `45-6=?`.

<table border="0" cellspacing="24" cellpadding="12">
  <tbody>
    <tr>
      <td style="border:none;padding:18px;"><img src="assets/captcha-sample-2.png" alt="Handwritten captcha" width="290" height="90"></td>
      <td style="border:none;padding:18px;"><img src="assets/captcha-sample-3.png" alt="Clean captcha" width="290" height="90"></td>
    </tr>
    <tr>
      <td style="border:none;padding:18px;"><img src="assets/captcha-sample-4.png" alt="Distorted captcha" width="290" height="90"></td>
      <td style="border:none;padding:18px;"><img src="assets/captcha-sample-1.png" alt="Math captcha" width="290" height="90"></td>
    </tr>
  </tbody>
</table>

## Model details

- **Architecture:** Vision Encoder-Decoder / TrOCR
- **Base model:** `microsoft/trocr-base-printed`
- **Task:** Image-to-text OCR
- **Target geometry:** 130x30 RGB images
- **Output:** Mathematical expressions without spaces

## Training configuration

| Parameter | Value |
|---|---:|
| Epochs | 5 |
| Learning rate | 5e-6 |
| Batch size | 8 |
| Gradient accumulation | 2 |
| Operator-token weight | 3.0 |
| Generation beams | 4 |
| Maximum output length | 32 |

## Usage

```python
from PIL import Image
from transformers import TrOCRProcessor, VisionEncoderDecoderModel

repo_id = "arkhabbazan/ocr-match-captcha"
processor = TrOCRProcessor.from_pretrained(repo_id, token=True)
model = VisionEncoderDecoderModel.from_pretrained(repo_id, token=True)

image = Image.open("captcha.jpeg").convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values, num_beams=4, max_length=32)
prediction = processor.batch_decode(
    generated_ids, skip_special_tokens=True
)[0]
print(prediction.replace(" ", ""))
```

## Limitations

- Training data is primarily synthetic.
- Unseen fonts and layouts may reduce accuracy.
- The model is intended for short expressions, not document OCR.
- Usage must be authorized and compliant with applicable policies.