In this blog post we will discuss about 4 best open source ocr library. Also we will discuss about importance of ocr, how ocr works.

Introduction

OCR also called as Optical Character Recognition (OCR) is a process that helps users to extract text from images, PDFs or scanned documents (a form or a receipt) and convert that text into a editable format.
Suppose you want to search some information from a image, you can not do that directly from image. Using OCR, you can get the text and you can search and get some information. OCR is used for digitization. Many companies are looking for automating documentation, and OCR plays a important role in processing image-based documents.

Importance of OCR

OCR is used to extract data from all types of business documents ( like forms, Receipts, invoices, legal documents etc. ) and send this text data to other business applications for further analysis.
The benefits of adopting OCR technology:

1. Converts as searchable Documents
2. Reduce Space: Image takes more storage space than the text format data
3. Reduce time: Manually getting the information from the image takes longer time but ocr takes very less time.
4. Reduce cost: It reduces the cost because ocr reduce humane effort and also the space of storage

How does OCR work?

The OCR tools or OCR technology works by using the following steps:

1. Image Pre-Processing
   De-skew: This is the approach in which scanned document and/or image is straightened and fix alignment issues means it correctness the angle.
   Binarization: Binarization is the method of converting scan document or image to black and white. This is one way to separate text from the background.
   Removes noise from the image and cleaning up the image.
   Normalization: normalization is a process that reduces noise by adjusting the pixels intensity value.
2. Segmentation: Segmentation involves the following steps:
   Word and text line detection:
   Script recognition : Identifies the script based on paragraphs, text lines, words, and characters. It is uses for multi-language OCR.
3. Character Recognition
   Matrix matching: Matrix matching algorithm finds the best match for each character with a library of character matrices.
   Feature recognition: recognizes the text patterns and features of characters like character’s size, height, shape etc
4. Postprocessing: This step is all about converting and extracting text data into a computerized file

4 Best open source OCR libraries

Below are the four best open source ocr library.

1.PaddleOCR:

PaddleOCR is an ocr framework or toolkit. PaddleOCR provides multilingual practical OCR tools that helps the user to train different models. PaddleOCR provides many high-quality pretrained models. It also provides Text detection, Text direction classifier and Text recognition. PaddleOCR is Multilingual OCR. It supports both CPU and GPU. For faster computing, GPU is preferred. PaddleOCR consists of an ultra-lightweight and general OCR model, integrating OCR algorithms like:

Text Detection Models: EAST, DB, SAST, PSENet, ECENet
Text Recognition Models: CRNN, SRN, NRTR, SVTR, ABINet
End-to-End Model: PGNet

2.Tesseract OCR:

Tesseract is one of the most well-known optical character recognition open-source library. Its developed in C++ and has wrappers which are available for Python, Java etc. Tesseract OCR recognizes text from more than 100 languages.
Tesseract supports Unicode (default utf-8), many image formats such as PNG, JPEG etc. Tesseract provides different types of output formats such as TEXT, TSV, and read-only text. Tesseract provides various options which can improve the performance. Tesseract 4 introduces LSTM models for Text recognition. You can use the Tesseract 3 Legacy mode or Combine Legacy + LSTM by using the OEM option

0 Legacy engine only.
1 Neural nets LSTM engine only.
2 Legacy + LSTM engines.
3 Default, based on what is available.

3.EasyOCR:

EasyOCR is a python library for extracting text from image. It supports 80+ languages. EasyOCR is Well suited for Scene Text Recognition. It uses Pytorch Deep learning models for Detection, Recognition. EasyOCR supports GPU and batch prediction. EasyOCR also supports several image formats. EasyOCR integrates OCR algorithms like:
Text Detection Model: Character Region Awareness For Text Detection (CRAFT)
Text Recognition Model: ResNet is for feature extraction, long short-term memory (LSTM) is for sequence labeling, and connectionist temporal classification (CTC) is for decoding (CRNN for end-to-end trainable model).

4.MMOCR

MMOCR is an open-source toolbox based on PyTorch. MMOCR offers a pipeline for text detection and recognition, as well as downstream tasks like NER and IE. MMOCR Model Zoo support below algorithms
Text Detection Models: DBNet,DBNetpp,DRRG, FCENet, Mask R-CNN,PANet,PSENet,Textsnake
Text Recognition Models: ABINet,CRNN,MASTER, NRTR, RobustScanner,SAR,SATRN,SegOCR,CRNN-STN