# An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet +
## 3 Background
### Optical Character Recognition  
The objective of character recognition is to enable a machine to recognise characters by using optical devices, such as a camera. The machine should be able to capture the image of the character and associate it with its corresponding symbolic identity [40]. Optical Character Recognition (OCR) technique can be found as early as 1900 where the scientist [PERSON] successfully implemented the idea and with the invention of modern computers, the research into this field has increased a lot. The OCR was initially developed to assist the visually challenged individuals to read the text, and then it was adopted into other application context such as banking, health and security in combination to Machine Learning (ML). A common OCR application is handwriting recognition, centred on smart devices that are capable of transforming handwritten text into its digital counterpart [41]. OCR is utilised to recognise languages of different origins not only in Roman script [42] but also in the case of degraded language documents. Another use of OCR to recognise handwriting is presented in [43], where the authors used the n-gram model, which produces groups of words that overlay on each other. The authors extended such model and utilised the likelihood score, from a statistical machine translation system, as main feature, so as to be able to capture the image of the words and to translate each image into other languages. A further application of OCR is provided in [44], which describes a method to recognise Bangla handwritten numerical characters using local binary pattern. Whereas in [45], the authors used OCR technique to detect printed English and Fraktur text using Long Short Term Memory (LSTM). The authors described that the handwritten content may vary in the position and baseline of the text, as a consequence a text line normalisation approach has been used to uniformly position the text. Such normalisation approach was based on an dictionary, containing data generated on the basis of the connected component shapes and associated baseline. For recognition and classification, the authors implemented 1-D Bidirectional Long Short Term Memory model, in order to establish the ground-truth align \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Technique** & **Contribution** & **Evaluation** & **Dataset** \\ \hline
[34] & RBNN, TF & Hashtag-centred detection of ISIS groups & Unspecified & EMNLP19 \\ \hline
[35] & TF-IDF, SVM, KNN & Text-based detection of Radical users & Accuracy: 94\% & Acquired \\ \hline
[36] & Word2 vec, doc2 vec, ANN & Text analysis of Sunni extremist content & Accuracy: 90\% & Acquired \\ \hline
[37] & Neural Network & Data collection and text analysis & Accuracy: 63\% & Custom \\ \hline
[38] & ELMo, BERT, and RoBERTa & Text analysis propaganda detection & Precision: 66\% & EMNLP19 \\ \hline
[39] & K-Means & Classification of Pro-ISIS users & Unspecified & Acquired \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mixed code related works. ment by using a forward-backward algorithm, which also provides a decoding mechanism to map the frame network upon a sequence of symbols. Further applications of OCR, for supporting the recognition of handwritten digits, are presented in literature. For example in [46], the use of Random Forest, Decision Tree and Hoeffding Tree as classifiers have been proposed to build a tool that can easily recognise the digits. For this, they utilised the handwritten digit dataset of MNIST, that is preprocessed before being used, in order to extract potential characterising features. In such work, a comparative study among the different classifiers has been conducted by concluding that the Hoeffding tree was the most performing one by reaching 73% accuracy. Another approach to support character recognition was based on the use of the Tesseract OCR open source engine to train a Tamil OCR model [47]. In particular, a segmentation approach was adopted by using a box file system.