Create README.md

README.md

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+---
+language:
+- th
+- en
+metrics:
+- cer
+tags:
+- easyocr
+- image-to-text
+pipeline_tag: image-to-text
+library_name: transformers
+license: apache-2.0
+---
+# PalOCR Model
+
+## Introduction
+
+PalOCR is a CRNN ('None-VGG-BiLSTM-CTC') model trained 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).
+
+## How to Use
+
+Here’s how to use this model in Python:
+
+```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)