Package: nocv2easyocr
EasyOCRReady-to-use OCR with 80+supported languagesand all popular writing scripts including: Latin, Chinese, Arabic, Devanagari, Cyrillic, etc.Try Demo on our websiteIntegrated intoHuggingface Spaces 🤗usingGradio. Try out the Web Demo:What's new15 September 2022 - Version 1.6.2Add CPU support for DBnetDBnet will only be compiled when users initialize DBnet detector.1 September 2022 - Version 1.6.1Fix DBnet path bug for WindowsAdd new built-in modelcyrillic_g2. This model is a new default for Cyrillic script.24 August 2022 - Version 1.6.0Restructure code to support alternative text detectors.Add detectorDBnet, seepaper. It can be used by initializing like thisreader = nocv2easyocr.Reader(['en'], detect_network = 'dbnet18').2 June 2022 - Version 1.5.0Add trainer for CRAFT detection model (thanks@gmuffiness, seePR)9 April 2022 - Version 1.4.2Update dependencies (opencv and pillow issues)11 September 2021 - Version 1.4.1Add trainer folderAddreadtextlangmethod (thanks@arkya-art, seePR)Extendrotation_infoargument to support all possible angles (thanksabde0103, seePR)29 June 2021 - Version 1.4Instructionson training/using custom recognition modelsExampledatasetfor model trainingBatched image inference for GPUs (thanks@SamSamhuns, seePR)Vertical text support (thanks@interactivetech). This is for rotated text, not to be confused with vertical Chinese or Japanese text. (seePR)Output in dictionary format (thanks@A2va, seePR)30 May 2021 - Version 1.3.2Faster greedy decoder (thanks@samayala22)Fix bug when a text box's aspect ratio is disproportional (thanksiQuarticfor bug report)20 April 2021 - Version 1.3.1Add support for PIL image (thanks@prays)Add Tajik language (tjk)Update argument setting for command lineAddx_thsandy_thsto control merging behavior whenparagraph=True21 March 2021 - Version 1.3Second-generation models: multiple times smaller size, multiple times faster inference, additional characters and comparable accuracy to the first generation models.
EasyOCR will choose the latest model by default but you can also specify which model to use by passingrecog_networkargument when creating aReaderinstance.
For example,reader = nocv2easyocr.Reader(['en','fr'], recog_network='latin_g1')will use the 1st generation Latin modelList of all models:Model hubRead all release notesWhat's coming nextHandwritten text supportExamplesInstallationInstall usingpipFor the latest stable release:pipinstalleasyocrFor the latest development release:pipinstallgit+https://github.com/JaidedAI/nocv2easyocr.gitNote 1: For Windows, please install torch and torchvision first by following the official instructions herehttps://pytorch.org. On the pytorch website, be sure to select the right CUDA version you have. If you intend to run on CPU mode only, selectCUDA = None.Note 2: We also provide a Dockerfilehere.Usageimporteasyocrreader=nocv2easyocr.Reader(['ch_sim','en'])# this needs to run only once to load the model into memoryresult=reader.readtext('chinese.jpg')The output will be in a list format, each item represents a bounding box, the text detected and confident level, respectively.[([[189,75],[469,75],[469,165],[189,165]],'愚园路',0.3754989504814148),([[86,80],[134,80],[134,128],[86,128]],'西',0.40452659130096436),([[517,81],[565,81],[565,123],[517,123]],'东',0.9989598989486694),([[78,126],[136,126],[136,156],[78,156]],'315',0.8125889301300049),([[514,126],[574,126],[574,156],[514,156]],'309',0.4971577227115631),([[226,170],[414,170],[414,220],[226,220]],'Yuyuan Rd.',0.8261902332305908),([[79,173],[125,173],[125,213],[79,213]],'W',0.9848111271858215),([[529,173],[569,173],[569,213],[529,213]],'E',0.8405593633651733)]Note 1:['ch_sim','en']is the list of languages you want to read. You can pass
several languages at once but not all languages can be used together.
English is compatible with every language and languages that share common characters are usually compatible with each other.Note 2: Instead of the filepathchinese.jpg, you can also pass an OpenCV image object (numpy array) or an image file as bytes. A URL to a raw image is also acceptable.Note 3: The linereader = nocv2easyocr.Reader(['ch_sim','en'])is for loading a model into memory. It takes some time but it needs to be run only once.You can also setdetail=0for simpler output.reader.readtext('chinese.jpg',detail=0)Result:['愚园路','西','东','315','309','Yuyuan Rd.','W','E']Model weights for the chosen language will be automatically downloaded or you can
download them manually from themodel huband put them in the '~/.EasyOCR/model' folderIn case you do not have a GPU, or your GPU has low memory, you can run the model in CPU-only mode by addinggpu=False.reader=nocv2easyocr.Reader(['ch_sim','en'],gpu=False)For more information, read thetutorialandAPI Documentation.Run on command line$easyocr-lch_simen-fchinese.jpg--detail=1--gpu=TrueTrain/use your own modelFor recognition model,Read here.For detection model (CRAFT),Read here.Implementation RoadmapHandwritten supportRestructure code to support swappable detection and recognition algorithms
The api should be as easy asreader=nocv2easyocr.Reader(['en'],detection='DB',recognition='Transformer')The idea is to be able to plug in any state-of-the-art model into nocv2easyocr. There are a lot of geniuses trying to make better detection/recognition models, but we are not trying to be geniuses here. We just want to make their works quickly accessible to the public ... for free. (well, we believe most geniuses want their work to create a positive impact as fast/big as possible) The pipeline should be something like the below diagram. Grey slots are placeholders for changeable light blue modules.Acknowledgement and ReferencesThis project is based on research and code from several papers and open-source repositories.All deep learning execution is based onPytorch. :heart:Detection execution uses the CRAFT algorithm from thisofficial repositoryand theirpaper(Thanks @YoungminBaek from@clovaai). We also use their pretrained model. Training script is provided by@gmuffiness.The recognition model is a CRNN (paper). It is composed of 3 main components: feature extraction (we are currently usingResnet) and VGG, sequence labeling (LSTM) and decoding (CTC). The training pipeline for recognition execution is a modified version of thedeep-text-recognition-benchmarkframework. (Thanks@ku21fanfrom@clovaai) This repository is a gem that deserves more recognition.Beam search code is based on thisrepositoryand hisblog. (Thanks@githubharald)Data synthesis is based onTextRecognitionDataGenerator. (Thanks@Belval)And a good read about CTC from distill.pubhere.Want To Contribute?Let's advance humanity together by making AI available to everyone!3 ways to contribute:Coder:Please send a PR for small bugs/improvements. For bigger ones, discuss with us by opening an issue first. There is a list of possible bug/improvement issues tagged with'PR WELCOME'.User:Tell us how EasyOCR benefits you/your organization to encourage further development. Also post failure cases inIssue  Sectionto help improve future models.Tech leader/Guru:If you found this library useful, please spread the word! (SeeYann Lecun's postabout EasyOCR)Guideline for new language requestTo request a new language, we need you to send a PR with the 2 following files:In foldereasyocr/character,
we need 'yourlanguagecode_char.txt' that contains list of all characters. Please see format examples from other files in that folder.In foldereasyocr/dict,
we need 'yourlanguagecode.txt' that contains list of words in your language.
On average, we have ~30000 words per language with more than 50000 words for more popular ones.
More is better in this file.If your language has unique elements (such as 1. Arabic: characters change form when attached to each other + write from right to left 2. Thai: Some characters need to be above the line and some below), please educate us to the best of your ability and/or give useful links. It is important to take care of the detail to achieve a system that really works.Lastly, please understand that our priority will have to go to popular languages or sets of languages that share large portions of their characters with each other (also tell us if this is the case for your language). It takes us at least a week to develop a new model, so you may have to wait a while for the new model to be released.SeeList of languages in developmentGithub IssuesDue to limited resources, an issue older than 6 months will be automatically closed. Please open an issue again if it is critical.Business InquiriesFor Enterprise Support,Jaided AIoffers full service for custom OCR/AI systems from implementation, training/finetuning and deployment. Clickhereto contact us.