# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Preprocessing
#### 3.1.1 Binarization and Thinning  
Binarization converts a grayscale or color image into a binary image, representing each pixel as either black or white. It is an essential preprocessing step that helps to segment the text from the background and increase the contrast between the characters and the background. The objective of binarization is to transform the input image into a binary format that enhances the visibility of the characters and makes them more easily recognized by the OCR system. Various binarization techniques are used in OCR, including thresholding, adaptive thresholding, and [PERSON]'s method. A method for preprocessing images of historical documents for OCR and search includes image binarization, skew correction, and line segmentation. The method was tested on a dataset of historical documents. The results show that it improves the accuracy of OCR by reducing errors caused by skew and noise and can be effectively applied to historical documents of various types. The binarization step of the method converts the image into a black-and-white image to make it easier for OCR software to recognize the text [52]. \begin{table}
\begin{tabular}{l l l} \hline
**Technique** & **Method** & **Brief Description** \\ \hline \multirow{6}{*}{Preprocessing} & Binarization & Transforms the input image into a binary format \\  & Keystone Correction & Aligns the distortion on the edges of the image \\  & Skew correction & Corrects the angle of the rotated text \\  & Denoising & Filters-out the extra-noisy pixels from the image \\  & Dilation & Restores an eroded image by cleaning it up \\  & Erosion & Removes object boundaries and unwanted parts in images \\  & Thinning & Reduces thickness of objects by removing boundary pixels \\  & Upscaling & Enhances the resolution of the image \\ \hline \multirow{3}{*}{Segmentation} & Line & Image is divided into lines for line-by-line processing \\  & Word & Each line is divided into words using spacing methods \\  & Character & Image of each word is divided into individual characters \\ \hline \multirow{6}{*}{Recognition} & Template Matching & Matches an input image with predefined characters \\  & Feature Extraction & Extracts features and classifies image using learning algorithm \\  & Neural Networks & Uses interconnected neurons to predict text from image \\  & Deep Learning & Uses neural networks with many layers to learn patterns \\  & Decision Trees & Builds tree-like structure with decisions and consequences \\  & SVM & Constructs hyperplane separating image into different classes \\  & Naive Bayes & Uses Bayes’ theorem to classify an input image \\  & Random Forest & Builds multiple decision trees, combining their outputs \\  & CNN & Uses deep learning with convolutional layers to classify image \\  & RNN & Neural network for processing sequences (characters in OCR) \\  & kNN & Classifies image based on \(k\)-nearest neighbors’ majority class \\  & HT & Detects lines, circles, and edges from image for text extraction \\  & HOG & Computes image gradients in histograms and extract features \\  & HMM & Models transition probabilities of text for accurate recognition \\  & Profile Projection & Extracts character features using projection onto 1D axis \\ \hline \multirow{3}{*}{Postprocessing} & Spell-check & Error correction, text enhancement, and restoration \\  & Contextual Analysis & Analyses the surrounding words based on specific context \\  & Confidence Scoring & Assigns scores to words—higher score means more accurate \\  & Language Model & Uses large corpus of text to guess best word in context \\ \hline \multirow{3}{*}{Evaluation} & Character Error Rate & Percentage of characters incorrectly predicted \\  & Word Error Rate & Percentage of words incorrectly predicted \\  & Recognition Rate & Percentage of characters/words correctly recognized \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of techniques applicable in various OCR methods.