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README.md

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+---
+license: apache-2.0
+---
+
+# ta_PP-OCRv3_mobile_rec
+
+## Introduction
+
+ta_PP-OCRv3_mobile_rec is a text line recognition model within the PP-OCRv3_rec series, developed by the PaddleOCR team. The ta_PP-OCRv3_mobile_rec model is an Tamil-specific model trained based on PP-OCRv3_mobile_rec, and it supports Tamil  recognition. The key accuracy metrics are as follow:
+
+<table>
+<tr>
+<th>Model</th>
+<th>Recognition Avg Accuracy(%)</th>
+<th>GPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>CPU Inference Time (ms)<br/>[Normal Mode / High-Performance Mode]</th>
+<th>Model Storage Size (M)</th>
+<th>Introduction</th>
+</tr>
+<tr>
+<td>ta_PP-OCRv3_mobile_rec</td>
+<td>76.83</td>
+<td>5.23 / 0.75</td>
+<td>10.13 / 4.30</td>
+<td>8.0 M </td>
+<td>An ultra-lightweight Tamil recognition model trained based on the PP-OCRv3 recognition model, supporting Tamil and numeric character recognition.</td>
+</tr>
+
+</table>
+
+**Note**: If any character (including punctuation) in a line is incorrect, the entire line is marked as wrong. This ensures higher accuracy in practical applications.
+
+## Quick Start
+
+### Installation
+
+1. PaddlePaddle
+
+Please refer to the following commands to install PaddlePaddle using pip:
+
+```bash
+# for CUDA11.8
+python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
+
+# for CUDA12.6
+python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
+
+# for CPU
+python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
+```
+
+For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick).
+
+2. PaddleOCR
+
+Install the latest version of the PaddleOCR inference package from PyPI:
+
+```bash
+python -m pip install paddleocr
+```
+
+### Model Usage
+
+You can quickly experience the functionality with a single command:
+
+```bash
+paddleocr text_recognition \
+    --model_name ta_PP-OCRv3_mobile_rec \
+    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/ZfY9A5MSkLc1PjWjBtEDz.png
+```
+
+You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine.
+
+```python
+from paddleocr import TextRecognition
+model = TextRecognition(model_name="ta_PP-OCRv3_mobile_rec")
+output = model.predict(input="ZfY9A5MSkLc1PjWjBtEDz.png", batch_size=1)
+for res in output:
+    res.print()
+    res.save_to_img(save_path="./output/")
+    res.save_to_json(save_path="./output/res.json")
+```
+
+After running, the obtained result is as follows:
+
+```json
+{'res': {'input_path': '/root/.paddlex/predict_input/ZfY9A5MSkLc1PjWjBtEDz.png', 'page_index': None, 'rec_text': 'தமிழ்பலவரிஉைர', 'rec_score': 0.9988247156143188}}
+```
+
+For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/text_recognition.html#iii-quick-start).
+
+### Pipeline Usage
+
+The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.
+
+#### PP-OCRv3
+
+The general OCR pipeline is used to solve text recognition tasks by extracting text information from images and outputting it in string format. And there are 5 modules in the pipeline: 
+* Document Image Orientation Classification Module (Optional)
+* Text Image Unwarping Module (Optional)
+* Text Line Orientation Classification Module (Optional)
+* Text Detection Module
+* Text Recognition Module
+
+Run a single command to quickly experience the OCR pipeline:
+
+```bash
+paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/7uts2DTGTsWXcPTJb87JQ.png \
+    --text_recognition_model_name ta_PP-OCRv3_mobile_rec \
+    --use_doc_orientation_classify False \
+    --use_doc_unwarping False \
+    --use_textline_orientation True \
+    --save_path ./output \
+    --device gpu:0 
+```
+
+Results are printed to the terminal:
+
+```json
+{'res': {'input_path': '/root/.paddlex/predict_input/7uts2DTGTsWXcPTJb87JQ.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': True, 'use_textline_orientation': True}, 'doc_preprocessor_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_orientation_classify': False, 'use_doc_unwarping': False}, 'angle': -1}, 'dt_polys': array([[[ 7,  3],
+        ...,
+        [ 7, 31]],
+
+       [[ 7, 35],
+        ...,
+        [ 7, 63]]], dtype=int16), 'text_det_params': {'limit_side_len': 64, 'limit_type': 'min', 'thresh': 0.3, 'max_side_limit': 4000, 'box_thresh': 0.6, 'unclip_ratio': 1.5}, 'text_type': 'general', 'textline_orientation_angles': array([0, 0]), 'text_rec_score_thresh': 0.0, 'rec_texts': ['தமிழ்பல்வரிஉைர', 'ேசாதைன்வழக்தகள்'], 'rec_scores': array([0.97451222, 0.95028651]), 'rec_polys': array([[[ 7,  3],
+        ...,
+        [ 7, 31]],
+
+       [[ 7, 35],
+        ...,
+        [ 7, 63]]], dtype=int16), 'rec_boxes': array([[ 7, ..., 31],
+       [ 7, ..., 63]], dtype=int16)}}
+```
+
+The command-line method is for quick experience. For project integration, also only a few codes are needed as well:
+
+```python
+from paddleocr import PaddleOCR  
+
+ocr = PaddleOCR(
+    text_recognition_model_name="ta_PP-OCRv3_mobile_rec",
+    use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model
+    use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module
+    use_textline_orientation=True, # Use use_textline_orientation to enable/disable textline orientation classification model
+    device="gpu:0", # Use device to specify GPU for model inference
+)
+result = ocr.predict("https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/7uts2DTGTsWXcPTJb87JQ.png")  
+for res in result:  
+    res.print()  
+    res.save_to_img("output")  
+    res.save_to_json("output")
+```
+
+The default model used in pipeline is `PP-OCRv5_server_rec`, so it is needed that specifing to `ta_PP-OCRv3_mobile_rec` by argument `text_recognition_model_name`. And you can also use the local model file by argument `text_recognition_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/OCR.html#2-quick-start).
+
+## Links
+
+[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr)
+
+[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)