You can deploy this model and start earning money today!

PaddlePaddle/
el_PP-OCRv5_mobile_rec

Name: el_PP-OCRv5_mobile_rec
Availability: InStock

Views

No views yet

el_PP-OCRv5_mobile_rec

Introduction

el_PP-OCRv5_mobile_rec is one of the PP-OCRv5_rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of Greek. The key accuracy metrics are as follow:

Model	Accuracy (%)
el_PP-OCRv5_mobile_rec	89.28

Note: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications.

Quick Start

Installation

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.

PaddleOCR

Install the latest version of the PaddleOCR inference package from PyPI:

python -m pip install paddleocr

Model Usage

You can quickly experience the functionality with a single command:

bash

paddleocr text_recognition \
    --model_name el_PP-OCRv5_mobile_rec \
    -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/MGeBVIwRWCE_CzK2nnCo1.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="el_PP-OCRv5_mobile_rec")
output = model.predict(input="MGeBVIwRWCE_CzK2nnCo1.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:

{'res': {'input_path': '/root/.paddlex/predict_input/MGeBVIwRWCE_CzK2nnCo1.png', 'page_index': None, 'rec_text': 'Γεια σας! Ελπίζω να έχετε μια υπέροχη μέρα.', 'rec_score': 0.9820859432220459}}

The visualized image is as follows:

For details about usage command and descriptions of parameters, please refer to the Document.

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-OCRv5

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/fQsoDVY6IMM8rk8tQKVBt.png \
    --text_recognition_model_name el_PP-OCRv5_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/fQsoDVY6IMM8rk8tQKVBt.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([[[ 151,  165],
        ...,
        [ 151,  223]],

       ...,

       [[ 111, 1284],
        ...,
        [ 111, 1308]]], shape=(23, 4, 2), 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], shape=(23,)), 'text_rec_score_thresh': 0.0, 'return_word_box': False, 'rec_texts': ['ΑΘΛΗΤΙΣΜΟΣ', 'ΚΑΙ ΚΟΙΝΩΝΙΚΕΣ', 'ΕΠΙΣΤΗΜΕΣ', 'Greek', 'Journal', 'of', 'Sport', 'and', 'Social', 'Sciences', 'Τεύχος', 'Φεβρουάριος', '2024', 'ISSN:', '2945-0608', 'ΠΕΡΙΟΔΙΚΗΕΚΔΟΣΗΤΩΝ.', '', 'Εργαστηρίου Κοινωνικής Ανάλυσης και Εφαρμοσμένης Κοινωνικής', 'Ερευνας, Τμήμα Κοινωνιολογίας του Πανεπιστημίου Κρήτής', 'Τομέα Νεοελληνικής Κοινωνίας, Τμήμα Κοινωνιολογίας του Πάντειου', 'Πανεπιστημίου', 'Μεταπτυχιακού Προγράμματος Αθλιητικές Σπουδές: Κοινωνιολογία,', 'Ιστορία, Ανθρωπολογία», Ελληνικ Ανοικτ Πανεπιστήμιο'], 'rec_scores': array([0.99981022, ..., 0.95862782], shape=(23,)), 'rec_polys': array([[[ 151,  165],
        ...,
        [ 151,  223]],

       ...,

       [[ 111, 1284],
        ...,
        [ 111, 1308]]], shape=(23, 4, 2), dtype=int16), 'rec_boxes': array([[ 151, ...,  223],
       ...,
       [ 111, ..., 1309]], shape=(23, 4), dtype=int16)}}

If save_path is specified, the visualization results will be saved under save_path. The visualization output is shown below:

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="el_PP-OCRv5_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/fQsoDVY6IMM8rk8tQKVBt.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 el_PP-OCRv5_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.

Links

PaddleOCR Repo

PaddleOCR Documentation

PaddlePaddle/el_PP-OCRv5_mobile_rec

el_PP-OCRv5_mobile_rec

Introduction

Quick Start

Installation

Model Usage

Pipeline Usage

PP-OCRv5

Links

PaddlePaddle/
el_PP-OCRv5_mobile_rec