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--- |
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language: |
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- th |
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- en |
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metrics: |
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- cer |
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tags: |
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- easyocr |
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- image-to-text |
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pipeline_tag: image-to-text |
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library_name: easyocr |
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license: apache-2.0 |
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--- |
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# PalOCR Model |
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## Introduction |
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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. |
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## Training Dataset |
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Generated images of openthaigpt/thai-ocr-evaluation datasets using [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator). |
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Which can be found at [palocr-datasets](https://huggingface.co/datasets/vorkna/palocr-datasets) |
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## How to Use |
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Here’s how to use this model with EasyOCR: |
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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. |
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```python |
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import easyocr |
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reader = easyocr.Reader(["th", "en"], gpu=True, recog_network="palocr") |
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result = reader.readtext('text.jpg') |
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``` |
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## Model Performance Comparison |
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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). |
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| Category | EasyOCR | PalOCR | Tesseract | |
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|:--------------|-----------:|-----------:|-----------:| |
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| real_document | 0.220217 | **0.960289** | 0.915707 | |
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| scene_text | 0.35865 | **1.0211** | 2.408704 | |
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| handwritten | 0.409302 | **1.01395** | 1.032375 | |
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| document | 0.0871795 | **0.946154** | 0.761595 | |
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| document_enth | 0.275449 | **0.916168** | 1.061107 | |
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**Disclaimer**: While this model is train on generated images of evaluation datasets, It was train on roughly 1,000 of generated images. |
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# Key Insights |
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* 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. |
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* 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. |
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* The evaluation dataset is sourced from the [openthaigpt/thai-ocr-evaluation](https://huggingface.co/datasets/openthaigpt/thai-ocr-evaluation). |
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## Authors |
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- Vorakan Sumethsenee (vorkna@proton.me) |
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