OCR Softwares to extract text from images - A Brief Overview

OCR or Optical Character Recognition is the technology that enables businesses to process images with text in them, extract the text and convert it into a machine readable format. What this means is that a person can take pictures of receipts, invoices, number plates, shipping container numbers, etc and use OCR technology to extract useful information in these images and put them in a format that a computer can read, edit, index and store for future use. This kind of technology has several applications across industries.

meter reading using OCR

Having in-house teams building solutions to automate tasks related to invoice processing or form digitization is not always the most pragmatic solution. To cater to these requirements, several OCR softwares and tools have been made available that anyone can use. These services include paid as well as free software. Having a good software can make things more convenient for your employees, reduce the time consumed in doing certain data entry and verification tasks, reduce the money spent on paper and help organisations develop a homogeneous workflow driven by automation.

Comparisons of different OCR softwares

There are several services that provide OCR solutions - paid and free. These softwares are different from APIs which allow users to access OCR technology using the internet without having to install softwares on local machines. Some of the best OCR solutions that exist in the market include -

  • Nanonets OCR API
  • Abbyy FineReader
  • ReadIris
  • SimpleOCR
  • Google Vision API
  • Tesseract

Free and paid OCR software

Some free OCR services include -

Some paid OCR services include -

  • OmniPage Ultimate
  • Abby FineReader
  • Adobe Acrobat Pro DC
  • ReadIris
  • Rossum Data Capture

How to choose best OCR Software?

The comparison between different OCR providers needs to take into consideration several factors like the accuracy provided, support for different languages, robustness against blurry, noisy images, support for a variety of fonts and sizes, ability to deal with tilted text and text in the wild, etc. Besides these factors, it is important to note that most softwares in the OCR space let users use pre-built models that are supposed to be generalizable to any kind of data the user might want to deal with. This usually leads to bad accuracy and a lot of time spent on error correction and data verification before the text extracted from images can be stored and indexed to be made searchable for future use. Depending on the use case of an organisation, this hurdle can lead to varying increases in costs and time consumed.

How Nanonets OCR software is better?

The benefits of using Nanonets over other OCR APIs go beyond just better accuracy. Here are a few reasons you should consider using the Nanonets OCR API.

ocr software

Automated intelligent structured field extraction - Nanonets makes it easy to extract text, structure the relevant data into the fields required and discard the irrelevant data extracted from the image.

Works well with several languages - We can provide an automated end to end pipeline specific to your use case by allowing custom training and varying vocabulary of our models to suit your needs.

Performs well on text in the wild - Reading street signs, shipping container numbers, number plates are some of the use cases that involve images in the wild. Nanonets utilizes object detection methods to improve searching for text in an image as well as classifying them even in images with varying contrast levels, font sizes, and angles.

Train on your own data to make it work for your use-case - Get rid of the rigidity your previous OCR services forced your workflow into. Being able to use your own data for training broadens the scope of applications as well as enhances your model performance.

Continuous learning - With new data, you are faced with more edge cases where the model’s predictions are not very confident or in some cases, false. To overcome such roadblocks, Nanonets OCR API allows you to re-train your models with new data with ease, so you can automate your operations anywhere faster.

No in-house team of developers required - No need to worry about hiring developers and acquiring talent to personalize the technology for your business requirements. Nanonets will take care of your requirements, starting from the business logic to an end to end product deployed that can be integrated easily into your business workflow without worrying about the infrastructure requirements.

How to use the Nanonets API ?

The Nanonets OCR API allows you to build OCR models with ease. You can upload your data, annotate it, set the model to train and wait for getting predictions through a browser based UI without writing a single line of code, worrying about GPUs or finding the right architecture for your deep learning models.

Using the GUI: https://app.nanonets.com/

Below, we will give you a step-by-step guide to training your own model using the Nanonets API, in 9 simple steps.

Step 1: Clone the Repo

git clone https://github.com/NanoNets/nanonets-ocr-sample-python
cd nanonets-ocr-sample-python
sudo pip install requests
sudo pip install tqdm

Step 2: Get your free API Key

Get your free API Key from https://app.nanonets.com/#/keys

nanonets ocr api key screen

Step 3: Set the API key as an Environment Variable


Step 4: Create a New Model

python ./code/create-model.py

Note: This generates a MODEL_ID that you need for the next step

Step 5: Add Model Id as Environment Variable


Step 6: Upload the Training Data

Collect the images of object you want to detect. Once you have dataset ready in folder images (image files), start uploading the dataset.

python ./code/upload-training.py

Step 7: Train Model

Once the Images have been uploaded, begin training the Model

python ./code/train-model.py

Step 8: Get Model State

The model takes ~30 minutes to train. You will get an email once the model is trained. In the meanwhile you check the state of the model

watch -n 100 python ./code/model-state.py

Step 9: Make Prediction

Once the model is trained. You can make predictions using the model

python ./code/prediction.py PATH_TO_YOUR_IMAGE.jpg

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