Mmocr text detection models. 7 pp on average in “irregular text” benchmarks.
Pose estimation toolbox and benchmark. . Object detection toolbox and benchmark Aug 14, 2021 · To facilitate future research and industrial applications of text recognition-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of text detection, recognition and understanding. DO REMEMBER TO USE the corresponding deployment config file when trying to convert models of different tasks. The default OpenCV backend used in MMCV would read them and apply the rotation on the images. Conventional template matching based approaches fail to generalize well to document images of unseen templates, and are not robust against text recognition errors. The vertical text is roatated 90° right and brought into a horizontal format. 6+. py CUDA_VISIBLE_DEVICES = python (From left to right: original image, text detection and recognition result mmocr. txt │ │ ├── test_label_1015. MMOCRInferencer serves as a user-friendly interface for OCR, integrating text detection and text recognition. FewShot learning toolbox and benchmark. panet Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network [https: {task}: task in mmocr. MMOCR is an open-source toolbox based on PyTorch and MMDetection for text detection and text recognition. MhLiao/DB • • 20 Nov 2019. Real-time Scene Text Detection with Differentiable Binarization; DRRG; Fourier Contour Embedding for Arbitrary-Shaped Text Detection; Mask R-CNN MMOCR . As a result, SATRN outperforms existing STR models by a large margin of 5. MMOCR is an open-source toolbox based on PyTorch and mmdetection ten state-of-the-art text detection, recognition, and understanding algorithms, and provides extensive benchmarks and models trained on popular academic datasets. The processed text regions are cropped and passes through the MMOCR mode (pretrained) for performing OCR. In addition to (distributed) training and testing Apr 29, 2022 · Saved searches Use saved searches to filter your results more quickly Jan 21, 2022 · Hello, Thanks for such an awesome repository! My god, I wish I found this months ago. Users can specify the metric used in the validation and test phases by modifying the val_evaluator and test_evaluator fields in the configuration file. Note: For users who want to train models on CTW1500, ICDAR 2015/2017, and Totaltext dataset, there might be some images containing orientation info in EXIF data. Object detection toolbox and benchmark For users who want to train models on CTW1500, ICDAR 2015/2017, and Totaltext dataset, there might be some images containing orientation info in EXIF data. I am trying to train text detection model for single character detection. The modular design of MMOCR enables users to define their own optimizers, data preprocessors, and model components such as backbones, necks and heads as well as losses. rotated and multi-line text). 7 pp on average in “irregular text” benchmarks. From the initial setup to obtaining inference results, the entire process took approximately 50 minutes. model_ckpt: str: The path to a model checkpoint file. By implementing several models for detection and recognition, we can benefit from End-to-End OCR is achieved in docTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). 0 depends on the new foundational library for training deep learning models MMEngine, and therefore has an entirely different dependency chain compared with MMOCR 0. The regions containing symbols are passed through EAST text detection model (pretrained) to determine the orientation of the text i. txt │ │ ├── Challenge2_Training Aug 14, 2021 · We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction. To facilitate future research and industrial applications of text recognition-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of text detection, recognition and understanding. g. OCR (text spotting) (coming soon) For convenience, MMOCRInferencer provides both Python and command line interfaces. You can DTTR is a CNN-transformer hybrid text detection moder, achieving 0. Apr 14, 2023 · We take a simple try to combine two off-the-shelf OCR models in MMOCR with SAM to develop some OCR-related application demos, including SAM for Text, Text Removal and Text Inpainting. x, which will soon be deprecated by the end of 2022. As such, you can select the architecture used for text detection, and the one for text recognition from the list of available implementations. Absolutely wicked performance, it scrapes off text from logos, flyers, blurred text, etc. In this paper, we propose an end-to-end Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key information from unstructured document images. textdet. MMTracking . Foundational library for computer vision. For users who want to train models on CTW1500, ICDAR 2015/2017, and Totaltext dataset, there might be some images containing orientation info in EXIF data. Object detection toolbox and benchmark MMOCR 1. Object detection toolbox and benchmark To facilitate future research and industrial applications of text recognition-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of text detection, recognition and understanding. For example, if you want to perform OCR inference on demo/demo_text_ocr. #12 best model for Scene Text Detection on SCUT-CTW1500 (F-Measure metric) open-mmlab/mmocr 4,213 whai362/PSENet Apr 21, 2024 · In this article, we’re using three models from PaddleOCR: text detection, text recognition, and text direction. Scene text detection, an important step of scene text reading systems, has witnessed rapid development with convolutional neural networks. Train a Model¶ MMOCR implements distributed training with MMDistributedDataParallel. The goal of scene text detection is to develop algorithms that can robustly detect and and label text with bounding boxes in uncontrolled and complex environments, such as street signs, billboards, or license plates. 5% H-mean improvements and 20. Overview. txt │ │ ├── train_words │ ├── icdar_2011 │ │ ├── training_label. Survey on Image Preprocessing Techniques to Improve OCR Source code for mmocr. Not suitable for real-time performance. f'Setting OMP_NUM_THREADS environment variable for each process ' MMOCR is an open source toolkit based on PyTorch and MMDetection, supporting numerous OCR-related models, including text detection, text recognition, and key information extraction. ten state-of-the-art text detection, recognition, and understanding algorithms, and provides extensive benchmarks and models trained on popular academic datasets. see here for the detailed results. galfar/deskew - Deskew is a command line tool for deskewing scanned text documents. model_type 'recog', 'det' The model type of the config file. MMOCR is an open source toolkit based on PyTorch and MMDetection, supporting numerous OCR-related models, including text detection, text recognition, and key information extraction. x. MMFlow . However, the post-processing of binarization is essential for segmentation-based detection, which converts probability maps produced by a segmentation method {task}: task in mmocr. # Test a pretrained MMOCR model by calling tools/test. register_module class PSENet (SingleStageTextDetector): """The class for implementing PSENet text detector: Shape Robust Text Detection with Progressive Scale Expansion Network. Video perception toolbox and benchmark. MMCV . And we also provide a WebUI by gradio to give a better interaction. The pre-trained model provided achieves 80. @DETECTORS. The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction. To facilitate future research If you installed MMOCR from source, you can run the following in MMOCR’s root directory: python tools/infer. jpg --det DBNet --rec CRNN --show --print-result You should be able to see a pop-up image and the inference result printed out in the console upon successful verification. Jul 13, 2022 · Given that the text orientation is around 0 degree, my naive understanding believe that this can easily be implement using any off the shelf det model that available in mmocr. models; mmocr. MMOCR implements widely-used evaluation metrics for text detection, text recognition and key information extraction tasks based on the MMEngine: BaseMetric base class. 3D human parametric model Text Detection Models; Text Recognition Models; You are reading the documentation for MMOCR 0. register_module class DBNet (TextDetectorMixin, SingleStageTextDetector): """The class for implementing DBNet text detector: Real-time Scene Text Detection with Differentiable Binarization. 0% faster in inference speed than the SOTA model with a backbone of ResNet-50 on MMOCR. detectors. Key Information Extraction Models; Named Entity Recognition Models; Text Detection Models; Text Recognition Models; Text Detection Models. Differences from original paper Use ResNet-50 rather than PVANET; Use dice loss (optimize IoU of segmentation) rather than balanced cross entropy Real-time Scene Text Detection with Differentiable Binarization. MMFewShot . MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key Thus, we use the DBNet model. Aug 5, 2022 · The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction. Even if you have a well-rounded MMOCR 0. see models: PANet_IC15: Text detection algorithm: recog: see models: SAR: Text recognition algorithm: kie [1] see models: None: Key information extraction algorithm: config_dir: str: configs/ Path to the config directory where all the config files are located: det_config: str: None: Path to the custom config file of the selected det model: det Aug 14, 2021 · We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction. However, the post-processing of binarization is essential for segmentation-based detection, which converts probability maps produced by a segmentation method One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. OpenMMLab Text Detection, Recognition and Understanding Toolbox - quincylin1/mmocr-1 Jan 7, 2023 · The toolbox supports not only text detection and text recognition, but also their downstream tasks such as key information extraction. In addition, it supports widely-used academic datasets and provides many useful tools, assisting users in exploring various aspects of models and datasets and One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. It uses Hough transform to detect "text lines" in the image. English; Here is an example of using 8 GPUs to train a text detection model on the dev partition. Evaluation¶. Like for text detection, we selected and tested two text recognition models. One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. 83 F1-score on ICDAR 2015 Incidental Scene Text Detection Challenge using only training images from ICDAR 2015 and 2013. py:33: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. jpg with DBNet as the text detection model and CRNN as the text recognition model, you can simply run the following command: One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. Optical flow toolbox and benchmark. Scene Text Detection is a computer vision task that involves automatically identifying and localizing text within natural images or videos. In addition, it supports widely-used academic datasets and provides many useful tools, assisting users in exploring various aspects of models and datasets and Apr 7, 2022 · d:\ocr\mmocr-main\mmocr\utils\setup_env. 0. Environment Configuration ¶ default_scope = 'mmocr' env_cfg = dict ( cudnn_benchmark = True , mp_cfg = dict ( mp_start_method = 'fork' , opencv_num_threads = 0 ), dist_cfg = dict Conventional template matching based approaches fail to generalize well to document images of unseen templates, and are not robust against text recognition errors. Until a few years ago, I was quite happy with Tesseract, but they've fallen behind since then. Text detection. Modular Design ARGS Type Description; model_config: str: The path to a model config file. It is part of the OpenMMLab project. The base class has implemented basic add/delete/update/check functions and supports data migration between different devices, as well as dictionary-like and tensor-like operations, which also allows the interfaces of different algorithms to be unified. MMHuman3D . ├── mixture │ ├── coco_text │ │ ├── train_label. We recommend Data Structures and Elements¶. jpg with DBNet as the text detection model and CRNN as the text recognition model, you can simply run the following command: . txt │ │ ├── test_label_1095. register_module class DBNet (SingleStageTextDetector): """The class for implementing DBNet text detector: Real-time Scene Text Detection with Differentiable Binarization. e vertical or horizontal. Text can be detected using image-based or frequency-based algorithms. Model Zoo. We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction. In addition to (distributed) training and testing For text detection, the metric should be either ‘hmean-ic13’ or ‘hmean-iou’. Look into open-mmlab's MMOCR, does both detection and recognition, with English and Chinese alphabet support. We provide empirical analyses that illustrate the inner mechanisms and the extent to which the model is applicable (e. Model for text recognition. To support multilingual OCR tasks, MMOCR also releases Chinese text recognition models trained on industrial datasets 1. In addition, it supports widely-used academic datasets and provides many useful tools, assisting users in exploring various aspects of models and datasets and The MMOCR Inferencer experience: OCR integration in under an hour. Jul 4, 2023 · MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores. MMOCR implements 14 state-of-the-art algorithms, which is significantly more than all the existing open-source OCR projects we are aware of to date. MMDeploy supports models of two tasks of mmocr, one is text detection and the other is text-recogntion. Mar 4, 2023 · Particularly MMOCR provides a conduit for downstream activities like named entity recognition and key information extraction in addition to text detection and recognition. Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text. PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. IcdarDataset has implemented two evaluation metrics, hmean-iou and hmean-ic13, to evaluate the performance of text detection models, where hmean-iou is the most widely used metric which computes precision, recall and F-score based on IoU between ground truth and prediction. Here are the steps i followed Sep 1, 2022 · MMEngine . Generative model toolbox. MODELS. Foundational library for training deep learning models. Multiple Models. As a downstream task of OCR, KIE models are required to run with bounding box annotations indicating the locations of text instances, from which an ROI extractor extracts the cropped features for bbox_head to discover relations among them. md to prepare your datasets) Jul 11, 2021 · However, using CRAFT /EAST for Text Detection(Deep learning models and supports multilingual text detection) MMOCR; Related Articles. Furthermore, many of these methods initialize the model through pre-training on the ImageNet dataset [ 37 ] . Text detection is the technique of detecting text in a picture and then enclosing it with a rectangular bounding box. @inproceedings {wang2019shape, title = {Shape robust text detection with progressive scale expansion network}, author = {Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages = {9336--9345}, year OCR (text spotting) (coming soon) For convenience, MMOCRInferencer provides both Python and command line interfaces. The main branch works with PyTorch 1. datasets; Switch Language. For text recognition, the metric should be ‘acc’. To facilitate future research MMOCR - Text Detection, Text Recognition PyTorch toolbox demoIn this video I explore MMOCR. Modular Design. text_deskewing - Rotate text images if they are not straight for better text detection and recognition. We use the text detection model to detect text regions in the image, the text recognition model to recognize the text within the detected regions, and the text direction model to determine the orientation of the text. x environment before, you still need to create a new python environment for MMOCR 1. py demo/demo_text_ocr. Abstract¶. I would like to train a DB Text Detection model, validate it every iteration, and only save the models with the best performance. Nonetheless, two main challenges still exist and hamper its deployment to real-world applications. Image-based sequence recognition has been a long-standing research topic in computer vision. @inproceedings {wang2019shape, title = {Shape robust text detection with progressive scale expansion network}, author = {Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages = {9336--9345}, year Aug 17, 2021 · Hi @gaotongxiao, thanks for all the support and the awesome project. MMGeneration . In addition to using our well-provided pre-trained models, you can also train models on your own datasets. This document will take the text detection algorithm DBNet and the text recognition algorithm CRNN as examples to introduce the contents of Config in detail. MMOCR uses MMEngine: Abstract Data Element to encapsulate the data required for each task into data_sample. To facilitate future research For users who want to train models on CTW1500, ICDAR 2015/2017, and Totaltext dataset, there might be some images containing orientation info in EXIF data. Even though ABINet was published two years after SATRN, the latter simply outperforms the former in terms of speed and accuracy on irregular texts, like those found on ID photos. MMDetection . Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordinate system. Jan 14, 2022 · Text detection; Text recognition ; How MMOCR combines all above; Let’s first discuss text detection. Mar 1, 2024 · Most existing methods for obtaining OCR results involve a two-stage process, including a text detection model and a text recognition model, or directly utilizing an end-to-end text spotting model. Text detection, recognition and understanding toolbox. models. (Please refer to datasets. x The architecture of key information extraction (KIE) models is similar to text detection models, except for the extra feature extractor. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. Since the pretrain model unable to detect some of the character, I then try to train the dbnetpp from scratch using the a custom dataset, and validate the train model 其中: {task}: mmocr 中的任务 mmdeploy 支持 mmocr 中的文字检测(text detection)、文字识别(text recognition)任务中的模型。关于模型-任务的划分,请参考章节模型支持列表。 @inproceedings{Yu2023TurningAC, title={Turning a CLIP Model into a Scene Text Detector}, author={Wenwen Yu and Yuliang Liu and Wei Hua and Deqiang Jiang and Bo Ren and Xiang Bai}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, year={2023} } @article{Yu2024TurningAC, title={Turning a CLIP Model into a Scene Text Spotter}, author={Wenwen Yu and Yuliang Liu and Xingkui OpenMMLab Text Detection, Recognition and Understanding Toolbox - bmitu/mmocr-v1. In the next section, we will take you through the basic functions of MMOCR by training DBNet on the mini ICDAR 2015 dataset as an example. MMEngine . txt │ │ ├── Challenge1_Training_Task3_Images_GT │ ├── icdar_2013 │ │ ├── train_label. Apr 19, 2023 · MMOCR. MMPose . kv eo qp xk dx mk nv fm mi yd