Oodles AI

Oodles AI

1589965168

Handwritten Text Recognition Using Tensorflow and CNN

Tensorflow is an open-source platform for machine learning. It is a deep learning framework, we use TensorFlow to build OCR systems for handwritten text, object detection, and number plate recognition. This solves accuracy issues. As a well-positioned AI development company, Oodles AI explores how to build and deploy handwritten text recognition using TensorFlow and CNN from scratch.

Handwritten Text Recognition (HTR) systems power computers to receive and interpret handwritten input from sources such as scanned images. The systems are able to convert handwritten texts into digital text or simply can digitize, store, and extract valuable information for accurate analysis. At Oodles, we use tools like OpenCV and provide TensorFlow development services to build a Neural Network (NN) which is trained on line-images from the off-line HTR dataset.

This Neural Network (NN) model split the text written in the scanned image into segmented line images. These line-images are smaller than images of the complete page image. 9/10 of the words of a segmented line from the validation-set are correctly recognized and the character error rate is around 8%.

The network is made up of 5 CNN and 2 RNN layers and workflow can be divided into 3 steps-

  1. Create 5 Convolutional Neural Network (CNN ) layers

There are 5 CNN layers. First, the Convolutional layer with 5×5 filter kernels in the first 2 layers Second, the non-linear RELU function is there. Finally, a pooling layer. The output is a feature map.

  1. Create a Recurrent neural network (RNN) layers and return its output

Create and stack two RNN layers with 256 units each and a bidirectional RNN from the stacked layers. Get 2 output sequences forward and backward of size 32×256. The output Calculates loss value and also decodes into the final text.

  1. Create IAM-compatible dataset and train model

The data-loader expects the IAM dataset [5] in the data/ directory. Below are the steps to get dataset:

Register for free at this ki.inf.unibe
Download words/words.tgz and extract
Download ascii/words.txt.
Put words.txt into the data/ directory.

Learn more: Handwritten Text Recognition Using Tensorflow and CNN

#text recognition using tensorflow and cnn

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Buddha Community

Handwritten Text Recognition Using Tensorflow and CNN
Oodles AI

Oodles AI

1589965168

Handwritten Text Recognition Using Tensorflow and CNN

Tensorflow is an open-source platform for machine learning. It is a deep learning framework, we use TensorFlow to build OCR systems for handwritten text, object detection, and number plate recognition. This solves accuracy issues. As a well-positioned AI development company, Oodles AI explores how to build and deploy handwritten text recognition using TensorFlow and CNN from scratch.

Handwritten Text Recognition (HTR) systems power computers to receive and interpret handwritten input from sources such as scanned images. The systems are able to convert handwritten texts into digital text or simply can digitize, store, and extract valuable information for accurate analysis. At Oodles, we use tools like OpenCV and provide TensorFlow development services to build a Neural Network (NN) which is trained on line-images from the off-line HTR dataset.

This Neural Network (NN) model split the text written in the scanned image into segmented line images. These line-images are smaller than images of the complete page image. 9/10 of the words of a segmented line from the validation-set are correctly recognized and the character error rate is around 8%.

The network is made up of 5 CNN and 2 RNN layers and workflow can be divided into 3 steps-

  1. Create 5 Convolutional Neural Network (CNN ) layers

There are 5 CNN layers. First, the Convolutional layer with 5×5 filter kernels in the first 2 layers Second, the non-linear RELU function is there. Finally, a pooling layer. The output is a feature map.

  1. Create a Recurrent neural network (RNN) layers and return its output

Create and stack two RNN layers with 256 units each and a bidirectional RNN from the stacked layers. Get 2 output sequences forward and backward of size 32×256. The output Calculates loss value and also decodes into the final text.

  1. Create IAM-compatible dataset and train model

The data-loader expects the IAM dataset [5] in the data/ directory. Below are the steps to get dataset:

Register for free at this ki.inf.unibe
Download words/words.tgz and extract
Download ascii/words.txt.
Put words.txt into the data/ directory.

Learn more: Handwritten Text Recognition Using Tensorflow and CNN

#text recognition using tensorflow and cnn

Oodles AI

Oodles AI

1591866982

Handwritten Text Recognition Using Tensorflow

Tensorflow is an open-source platform for machine learning. It is a deep learning framework, we use TensorFlow to build OCR systems for handwritten text, object detection, and number plate recognition. This solves accuracy issues. As a well-positioned AI development company, Oodles AI explores how to build and deploy handwritten text recognition using TensorFlow and CNN from scratch.

Handwritten Text Recognition (HTR) systems power computers to receive and interpret handwritten input from sources such as scanned images. The systems are able to convert handwritten texts into digital text or simply can digitize, store, and extract valuable information for accurate analysis. At Oodles, we use tools like OpenCV and provide TensorFlow development services to build a Neural Network (NN) which is trained on line-images from the off-line HTR dataset.

This Neural Network (NN) model split the text written in the scanned image into segmented line images. These line-images are smaller than images of the complete page image. 9/10 of the words of a segmented line from the validation-set are correctly recognized and the character error rate is around 8%.

The network is made up of 5 CNN and 2 RNN layers and workflow can be divided into 3 steps-

  1. Create 5 Convolutional Neural Network (CNN ) layers

There are 5 CNN layers. First, the Convolutional layer with 5×5 filter kernels in the first 2 layers Second, the non-linear RELU function is there. Finally, a pooling layer. The output is a feature map.

  1. Create a Recurrent neural network (RNN) layers and return its output

Create and stack two RNN layers with 256 units each and a bidirectional RNN from the stacked layers. Get 2 output sequences forward and backward of size 32×256. The output Calculates loss value and also decodes into the final text.

Architecture

  1. Create IAM-compatible dataset and train model

The data-loader expects the IAM dataset [5] in the data/ directory. Below are the steps to get dataset:

Register for free at this fki.inf.unibe.ch
Download words/words.tgz and extract
Download ascii/words.txt.
Put words.txt into the data/ directory.
Create the directory data/words/.
Input the content (directories a01, a02, …) of words.tgz into data/words/.
Train the model from scratch

To train the model from scratch we go to the src/ directory of our project and execute this command on terminal python main.py --train. After training, validation is done on a validation set (the dataset is split into 95% of the samples used for training and 5% for validation as defined in the class DataLoader). Validation is done by executing the command python main.py –validate. Training on the CPU takes about 30 hours on a normal configuration system.

Learn more: Text Recognition Using Tensorflow

#handwritten text recognition using tensorflow

Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.

5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.

6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.

Read More

#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website

I am Developer

1597475640

Laravel 7 Full Text Search MySQL

Here, I will show you how to create full text search in laravel app. You just follow the below easy steps and create full text search with mysql db in laravel.

Laravel 7 Full Text Search Mysql

Let’s start laravel full-text search implementation in laravel 7, 6 versions:

  1. Step 1: Install Laravel New App
  2. Step 2: Configuration DB .evn file
  3. Step 3: Run Migration
  4. Step 4: Install Full Text Search Package
  5. Step 5: Add Fake Records in DB
  6. Step 6: Add Routes,
  7. Step 7: Create Controller
  8. Step 8: Create Blade View
  9. Step 9: Start Development Server

https://www.tutsmake.com/laravel-full-text-search-tutorial/

#laravel full text search mysql #laravel full text search query #mysql full text search in laravel #full text search in laravel 6 #full text search in laravel 7 #using full text search in laravel

A Demo Code Of Training and Testing using Tensorflow

ProbFace, arxiv

This is a demo code of training and testing [ProbFace] using Tensorflow. ProbFace is a reliable Probabilistic Face Embeddging (PFE) method. The representation of each face will be an Guassian distribution parametrized by (mu, sigma), where mu is the original embedding and sigma is the learned uncertainty. Experiments show that ProbFace could

  • improve the robustness of PFE.
  • simplify the calculation of the multal likelihood score (MLS).
  • improve the recognition performance on the risk-controlled scenarios.

#machine learning #tensorflow #testing #a demo code of training and testing using tensorflow #a demo code of training #testing using tensorflow