# Machine Learning Approach for Arabic Handwritten Recognition
###### Abstract  
Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been conducted on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. In the new model, the architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of a similar type with a character error rate of 13.2% and word error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods. machine learning; handwritten recognition systems; Arabic handwriting; BiLSTM; ResNet; natural language processing +
Footnote †: journal: Applied sciences  
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The probability given by CTC is defined as for input sequence \(Y\), where \(Y=y_{1},\ldots,y_{T}\), and \(T\) is the length of the sequence, the output is the probability of \(\pi\), which is defined in Equation (2) as:  
\[p(\pi|Y)=\prod_{t=1}^{T}y_{\pi_{t}}^{t}\] (2)  
where \(y_{\pi_{t}}^{t}\) is the probability of having a character \(\pi_{t}\) at time step t [62]. A sequence-to-sequence mapping function \(M\) is defined on the sequence \(\pi\). The mapping function \(M\) maps \(\pi\) onto l, where l is the final prediction output, by removing the repeated characters first, and then removing the blanks. For example, \(M\) maps \" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Finally, we added a 3-gram language model to our system to improve the recognition accuracy. The KenLM toolkit, a fast and memory-efficient language model, is used to build a 3-gram language model. The language model compares the weights assigned by CTC and LM. The predicted word with the highest weight will be replaced. For optimization, we adopted the Adadelta optimizer, which is a robust learning rate method that does not require the manual setting of a learning rate. We set the training batch size to 24, and all images were scaled to 1048x64 in both training and testing. The data were split to 80% for training, 10% for validation, and the remaining 10% for testing.