Handwriting recognition is a technology that converts handwritten text into machine-readable text. R programming language is popularly known for its statistical analysis of multiple libraries and packages making it easy and user-friendly. but can also provide handwriting recognition. Handwriting recognition is a useful technology that helps in many sectors such as medicine, education, etc to identify handwritten letters and turn them into machine-readable alphabets and words. This article explores the possibility of handwriting recognition libraries in R and provides a detailed implementation example.

Overview of Handwriting Recognition

Handwriting recognition involves several steps:

1. Image Acquisition: Capturing or scanning the handwritten text. Here, we will upload the text with the help of the path.
2. Preprocessing: Enhancing the image for better recognition.
3. Text Recognition: Using OCR (Optical Character Recognition) to identify and extract the text.
4. Postprocessing: Refining the recognized text to improve accuracy.

Libraries and Packages in R for Handwriting Recognition

R provides multiple packages for handwriting recognition:

• tesseract: This is an OCR engine that helps in character recognition from the provided images.
• magick: This is an image-processing library.
• keras and tensorflow: These two are deep learning models that help in training algorithms.
• imager: This is also an image-processing library widely used for its computer vision in the R programming language.
• EBImage: This library provides image processing and analysis capabilities to R.

Now we discuss step by step Implementation Handwriting Recognition Library in R Programming Language.

Step 1: Loading and Installing the libraries

Ensure you have R and RStudio installed, and then install the necessary packages:

# Installing Packages

install.packages("tesseract")
install.packages("magick")
install.packages("keras")
install.packages("tensorflow")
install.packages("imager")
install.packages("EBImage")

# Loading Libraries

library(tesseract)
library(magick)
library(keras)
library(tensorflow)
library(imager)
library(EBImage)

Step 2: Load and Preprocess the Image

Now we will load the image that we are using for this process.

# Load the image using magick
image_path <- "handwritten_note.jpeg"
image <- image_read(image_path)

# Display the original image
print(image)

# Preprocess the image: convert to grayscale and increase contrast
image_preprocessed <- image %>%
  image_convert(colorspace = 'gray') %>%
  image_contrast(sharpen = 1)

# Display the preprocessed image
print(image_preprocessed)

Output:

# A tibble: 1 × 7
  format width height colorspace matte filesize density
  <chr>  <int>  <int> <chr>      <lgl>    <int> <chr>  
1 JPEG     564    817 sRGB       FALSE    43179 72x72  

Handwritten Letters Before Pre-Processing

# A tibble: 1 × 7
  format width height colorspace matte filesize density
  <chr>  <int>  <int> <chr>      <lgl>    <int> <chr>  
1 JPEG     564    817 Gray       FALSE        0 72x72  

Handwritten Letters After Pre-Processing

Step 3: Text Recognition with Tesseract

Now we Recognize text using Tesseract.

# Initialize the tesseract engine for English
eng <- tesseract("eng")

# Recognize text from the preprocessed image
text <- ocr(image_preprocessed, engine = eng)

# Print the recognized text
cat("Recognized Text:\n", text)

Output:

Recognized Text:

 Lowercase
aocdefghijkimnope
grstuvwxyt)9

ouine Letty
Aboedetgnisk
LmvepgraAtuuvwry 3

CAPS
ARCDEFGHIIKLMNOP
ARSTUVWKYZ

It read most of the letters correctly except a few alphabets that are not written clearly.

Common Debugging Issues

There are multiple issues that can come in our way of recognizing handwritten letters or words. Some of them are:

1. Poor Image Quality: The quality of the image highly decides how we can use it or if we will be able to decipher the letters and words written over it. For this, we must use a good quality picture.
2. Incorrect Text Recognition: This issue can come when the letters are not written properly like the example we had earlier. It couldn’t differentiate between ‘b’ and ‘o’ because it was written in the same way. We can use different processing technique to use if the model cannot recognize the words easily.
3. Package Installation: Make sure all necessary packages are correctly installed and loaded.
4. Model Convergence: If the model is not converging, try adjusting hyperparameters such as learning rate, batch size, or network architecture.

Best Practices for Handwriting Recognition

• High Quality Pictures: Use high quality images so that it is easier for the algorithm to recognize the words and letters.
• Preprocessing: Preprocessing is important to enhance the quality of the image to get better recognition.
• Training Data: If dealing with specific types of handwriting, consider training a custom OCR model.
• Hyperparameter Tuning: Tuning the parameters help in modelling the algorithm better making it more efficient.

Conclusion

In this article, we discussed the ways to recognize letters from handwritten images using R programming Language. R offers many packages and libraries that helps in recognizing the letters and words from the image.

Handwriting Recognition Library-FAQs

What is OCR?

Optical Character Recognition is technique that turns pdfs, images and scannable document that can be searched and edited. OCR reads the document and turns it into a machine readable text.

Can “tesseract” recognize all types of handwriting?

tesseract is a powerful library that can identify a wide range of handwriting if the image is in good quality and the handwriting is readable.

How do I preprocess images for better OCR accuracy?

We can convert the pic in grayscale, adjust the contrast and brightness, resize the image for the model input while preprocessing the image.

How do I improve the accuracy of my custom handwriting recognition model?

Use large, diverse dataset can be helpful in improving the accuracy. Proper preprocessing can also be very helpful.