# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Segmentation
#### 3.2.3 Character Segmentation  
Character-level segmentation is a technique used to segment an image of a single word into individual letters and characters. It is an optional step depending on the context of the OCR system that is being used. It may be unnecessary if the text has separate letters within a word, as the letters and characters can be segmented in the previous step using a threshold. However, character-level segmentation must be performed if the text has cursive handwriting or a nature where letters are joined. A method for recognizing and transcribing text from a visual Arabic scripting news ticker from a broadcast stream is presented in [77]. The technique used in this research includes image processing techniques such as thresholding, morphological operations, and connected component analysis to segment the text from the background. It uses machine learning algorithms such as CNNs and long short-term memory (LSTM) to transcribe text. The research results show that the proposed method can accurately recognize and transcribe text from a visual Arabic scripting news ticker from a broadcast stream. The research uses several evaluation metrics, such as character error rate (CER) and word error rate (WER), to evaluate the performance of the proposed method. The results show that the proposed method outperforms traditional methods in recognition accuracy, achieving a lower CER and WER than traditional methods on the dataset used in the research. [PERSON] [78] discusses Arabic character segmentation approaches, including traditional methods such as vertical and horizontal projection, contour tracing and thinning, template matching, neural networks and HMM, holistic approaches and segmentation-free approaches, projection profile, baseline, contour tracing, graph theory, and morphology. The paper also discusses the challenges and limitations of each approach and suggests areas for future research. It also discusses the benefits and problems with character segmentation, especially in Arabic; problems are due to its cursive nature and the different shapes of each character depending on the word's appearance. Problems also occur because of datasets. Therefore, the Arabic Language Technology Center (ALTEC) have provided limited free access to a reliable dataset. A method for segmenting characters, letters, and digits from Arabic handwritten document images using a hybrid approach is presented in [79]. The method uses image processing techniques, such as thresholding, morphological operations, and connected component analysis, to segment the text from the background and to separate the characters. In addition, machine learning algorithms, including \(k\)-means clustering and a Random Forest Classifier, are used to classify the segments into individual characters. The research demonstrates that the proposed method can accurately segment characters from Arabic handwritten document images with a high degree of accuracy. The method outperforms traditional methods regarding segmentation accuracy, as evidenced by several evaluation metrics, including F-score, precision, and recall. The proposed method achieves a higher F-score, precision, and recall than traditional methods on the dataset used in the research. Morphological operators are also used for segmenting Arabic handwritten words [80]. The process involves using morphological operations, such as erosion and dilation, to extract the text from the background and segment the words. The technique used in this research is based on morphological operators, which perform operations such as erosion and dilation on binary images, to extract the text and separate the words. The method also uses image processing techniques, such as thresholding, to convert the image into a binary image and connected component analysis to identify and label connected regions corresponding to words. The research results show that the proposed method can accurately segment Arabic handwritten words with a high degree of accuracy.