{"id":13800799,"url":"https://github.com/IBM/MAX-OCR","last_synced_at":"2025-05-13T09:31:55.893Z","repository":{"id":43245561,"uuid":"218193312","full_name":"IBM/MAX-OCR","owner":"IBM","description":"MAX Optical Character Recognition","archived":false,"fork":false,"pushed_at":"2023-05-23T00:40:57.000Z","size":2169,"stargazers_count":50,"open_issues_count":5,"forks_count":30,"subscribers_count":17,"default_branch":"master","last_synced_at":"2025-05-07T05:42:15.560Z","etag":null,"topics":["docker-image","machine-learning","tesseract-ocr-engine"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/IBM.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-10-29T03:06:00.000Z","updated_at":"2025-04-15T05:26:09.000Z","dependencies_parsed_at":"2024-01-05T20:57:22.431Z","dependency_job_id":"4225450f-47ef-4883-bbf5-1bc9f9969e84","html_url":"https://github.com/IBM/MAX-OCR","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2FMAX-OCR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2FMAX-OCR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2FMAX-OCR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IBM%2FMAX-OCR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/IBM","download_url":"https://codeload.github.com/IBM/MAX-OCR/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253913244,"owners_count":21983283,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["docker-image","machine-learning","tesseract-ocr-engine"],"created_at":"2024-08-04T00:01:16.351Z","updated_at":"2025-05-13T09:31:53.268Z","avatar_url":"https://github.com/IBM.png","language":"Python","funding_links":[],"categories":["Data \u0026 AI"],"sub_categories":[],"readme":"[![Build Status](https://travis-ci.com/IBM/MAX-OCR.svg?branch=master)](https://travis-ci.com/IBM/MAX-OCR) [![Website Status](https://img.shields.io/website/http/max-ocr.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-ocr.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud)  \n[\u003cimg src=\"docs/deploy-max-to-ibm-cloud-with-kubernetes-button.png\" width=\"400px\"\u003e](http://ibm.biz/max-to-ibm-cloud-tutorial) \n\n# IBM Developer Model Asset Exchange: Optical Character Recognition\n\nThis repository contains code to instantiate and deploy an optical character recognition model. This model takes an\nimage of text as an input and returns the predicted text. This model was trained on 20 samples of 94 characters from 8\ndifferent fonts and 4 attributes (regular, bold, italic, bold + italic) for a total of 60,160 training samples. Please\nsee the paper [An Overview of the Tesseract OCR Engine](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/33418.pdf)\nfor more detailed information about how this model was trained.\n\nThe code in this repository deploys the model as a web service in a Docker container. This repository was developed as\npart of the [IBM Code Model Asset Exchange](https://developer.ibm.com/code/exchanges/models/) and the public API is\npowered by [IBM Cloud](https://ibm.biz/Bdz2XM).\n\n## Model Metadata\n\n| Domain        | Application                   | Industry | Framework  | Training Data          | Input Data Format |\n|---------------|-------------------------------|----------|------------|------------------------|-------------------|\n| Image \u0026 Video | Optical Character Recognition | General  | n/a        | Tesseract Data Files   | Image (PNG/JPG)   |\n\n\n## References\n\n* _Smith, Ray._ [\"An overview of the Tesseract OCR engine.\"](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/33418.pdf)\n    Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). Vol. 2. IEEE, 2007.\n\n## Licenses\n\n| Component | License | Link  |\n| ------------- | --------  | -------- |\n| This repository | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |\n| Model Code (3rd party) | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [Tesseract OCR Repository](https://github.com/tesseract-ocr/tesseract/blob/master/LICENSE) |\n| Test Samples | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [Sample README](samples/README.md)\n\n## Prerequisites\n\n* `docker`: The [Docker](https://www.docker.com/) command-line interface. Follow the\n[installation instructions](https://docs.docker.com/install/) for your system.\n* The minimum recommended resources for this model is 2GB Memory and 2 CPUs.\n\n# Deployment options\n\n* [Deploy from Quay](#deploy-from-quay)\n* [Deploy on Red Hat OpenShift](#deploy-on-red-hat-openshift)\n* [Deploy on Kubernetes](#deploy-on-kubernetes)\n* [Run Locally](#run-locally)\n\n## Deploy from Quay\n\nTo run the docker image, which automatically starts the model serving API, run:\n\n```bash\n$ docker run -it -p 5000:5000 quay.io/codait/max-ocr\n```\n\nThis will pull a pre-built image from the Quay.io container registry (or use an existing image if already cached locally) and run it.\nIf you'd rather checkout and build the model locally you can follow the [run locally](#run-locally) steps below.\n\n## Deploy on Red Hat OpenShift\n\nYou can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web\nconsole or the OpenShift Container Platform CLI [in this\ntutorial](https://developer.ibm.com/tutorials/deploy-a-model-asset-exchange-microservice-on-red-hat-openshift/),\nspecifying `quay.io/codait/max-ocr` as the image name.\n\n## Deploy on Kubernetes\n\nYou can also deploy the model on Kubernetes using the latest docker image on Quay.\n\nOn your Kubernetes cluster, run the following commands:\n\n```bash\n$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-OCR/master/max-ocr.yaml\n```\n\nThe model will be available internally at port `5000`, but can also be accessed externally through the `NodePort`.\n\nA more elaborate tutorial on how to deploy this MAX model to production on [IBM Cloud](https://ibm.biz/Bdz2XM) can be\nfound [here](http://ibm.biz/max-to-ibm-cloud-tutorial).\n\n## Run Locally\n\nTo build and deploy the model to a REST API using Docker, follow these steps:\n\n1. [Build the Model](#1-build-the-model)\n2. [Deploy the Model](#2-deploy-the-model)\n3. [Use the Model](#3-use-the-model)\n4. [Development](#4-development)\n5. [Cleanup](#5-cleanup)\n\n\n### 1. Build the Model\n\nClone the `MAX-OCR` repository locally. In a terminal, run the following command:\n\n```bash\n$ git clone https://github.com/IBM/MAX-OCR.git\n```\n\nChange directory into the repository base folder: \n\n```bash\n$ cd MAX-OCR\n```\n\nTo build the docker image locally, run:\n\n```bash\n$ docker build -t max-ocr .\n```\n\nAll required model assets will be downloaded during the build process. _Note_ that currently this docker image is CPU\nonly (we will add support for GPU images later).\n\n\n### 2. Deploy the Model\n\nTo run the docker image, which automatically starts the model serving API, run:\n\n```bash\n$ docker run -it -p 5000:5000 max-ocr\n```\n\nBy default, Cross-Origin Resource Sharing (CORS) is disabled. To _enable_ CORS support, include the following -e flag\nwith your run command:\n\n```bash\n$ docker run -it -e CORS_ENABLE='true' -p 5000:5000 max-ocr\n```\n\n\n### 3. Use the Model\n\nThe API server automatically generates an interactive Swagger documentation page. Go to `http://localhost:5000` to load\nit. From there you can explore the API and also create test requests.\n\nUse the `model/predict` endpoint to load a test image (you can use one of the test images from the `samples` folder) and\nget the predicted text for the image from the API.\n\n![pic](/docs/swagger-screenshot.png \"Swagger Screenshot\")\n\nYou can also test it on the command line, for example:\n\n_(using this scanned text image)_\n\n\u003cimg src=\"/samples/quick_start_watson_studio.jpg\" width=\"527\" height=\"209\" alt=\"Sample Image\"\u003e\n\n```bash\n$ curl -F \"image=@samples/quick_start_watson_studio.jpg\" -XPOST http://localhost:5000/model/predict\n```\n\nYou should see a JSON response like that below:\n\n```json\n{\n  \"status\": \"ok\",\n  \"text\": [\n    [\n      \"Quick Start with Watson Studio\"\n    ],\n    [\n      \"Watson Studio is IBM’s hosted notebook service, and you can create\",\n      \"a free account at https://www.ibm.com/cloud/watson-studio. Other\",\n      \"hosted notebook services can be used to run the noteooks as well,\",\n      \"but Watson Studio offers all of the frameworks and languages that\",\n      \"are used for this book’s examples. Once you have created an account\",\n      \"and logged in, you can begin by creating a project and notebook.\"\n    ]\n  ]\n}\n```\n\n### 4. Development\n\nTo run the Flask API app in debug mode, edit `config.py` to set `DEBUG = True` under the application settings. You will\nthen need to rebuild the Docker image (see [step 1](#1-build-the-model)).\n\n### 5. Cleanup\n\nTo stop the Docker container, type `CTRL` + `C` in your terminal.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FIBM%2FMAX-OCR","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FIBM%2FMAX-OCR","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FIBM%2FMAX-OCR/lists"}