README.md

---
language:
- th
- en
metrics:
- cer
tags:
- easyocr
- image-to-text
pipeline_tag: image-to-text
library_name: easyocr
license: apache-2.0
---
# PalOCR Model

## Introduction

PalOCR is a CRNN ('None-VGG-BiLSTM-CTC') model trained base from EasyOCR guideline with solely purpose of getting a better score of openthaigpt/thai-ocr-evaluation datasets due to limitation of author hardware.
![Model Comparisons](https://github.com/clovaai/deep-text-recognition-benchmark/raw/master/figures/trade-off.png)

## Training Dataset

Generated images of openthaigpt/thai-ocr-evaluation datasets using [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator).
Which can be found at [palocr-datasets](https://huggingface.co/datasets/vorkna/palocr-datasets)

## How to Use

Here’s how to use this model with EasyOCR:
Please download, extract and place palocr.py, palocr.yaml in the user_network_directory (default = ~/.EasyOCR/user_network) and place palocr.pth in model directory (default = ~/.EasyOCR/model) Once you place all 3 files in their respective places you can use this code to run model.

```python
import easyocr
reader = easyocr.Reader(["th", "en"], gpu=True, recog_network="palocr")
result = reader.readtext('text.jpg')
```

## Model Performance Comparison

This section details the performance comparison between the open-source ThaiTrOCR model and other widely-used OCR systems, namely EasyOCR and Tesseract. The table below highlights their respective performance across various document types based on the average Character Error Rate (CER).


| Category      |   EasyOCR  |   PalOCR   | Tesseract  |
|:--------------|-----------:|-----------:|-----------:|
| real_document |  0.220217  |   **0.960289** | 0.915707 |
| scene_text    |  0.35865   |   **1.0211**   | 2.408704 |
| handwritten   |  0.409302  |   **1.01395**  | 1.032375 |
| document      |  0.0871795 |   **0.946154** | 0.761595 |
| document_enth |  0.275449  |   **0.916168** | 1.061107 |

**Disclaimer**: While this model is train on generated images of evaluation datasets, It was train on roughly 1,000 of generated images.

# Key Insights

* Character Error Rate (CER): This metric evaluates the percentage of characters that were incorrectly predicted by the model. A lower CER indicates better performance. As shown in the table, ThaiTrOCR consistently outperforms EasyOCR and Tesseract across all document types, with a significantly lower average CER, making it the most accurate model in the comparison.
* Tesseract Limitation: It’s important to note that Tesseract only supports single-language input at a time in this comparison. For the purposes of this benchmark, it was tested using only the Thai language setting, which might have contributed to its higher CER values.
* The evaluation dataset is sourced from the [openthaigpt/thai-ocr-evaluation](https://huggingface.co/datasets/openthaigpt/thai-ocr-evaluation).

## Authors

- Vorakan Sumethsenee (vorkna@proton.me)