# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 2 Related Works  
With technological advances, cameras have become smaller and have been integrated into even mobile phones. This integration allows us to record whenever we want. In line with these developments, more and more data are obtained day by day. In this way, the ever-increasing volume of visual data drives more research in image processing and better results are obtained. In the field of image processing, these studies drive advancements in areas such as autonomous vehicles and robotic systems. At the same time, with the emergence of deep learning architectures in the field of image processing, especially in recent years, the successes in these areas are increasing exponentially. The studies on image processing not only pioneer new technologies but also serve the purpose of improving human life, which is the fundamental meaning of technology. In this context, studies such as sign language recognition [9] and lip reading [10; 11] aim to enhance the lives of individuals with disabilities. Lip-reading studies, which aim to enhance human life, have emerged as a crucial area of research in image processing, especially since hearing aids are less effective in noisy environments. Although a large number of data sets have been created in lip reading studies that have been studied for many years, it is seen that more studies have been done on certain data sets. The data sets created for lip reading can have different contents. It is seen that the content of the datasets is structured as letters, words, numbers, sentences or a mixture of these. The OuluVs2 dataset, contains 10 digit sequences repeated 3 times by 52 speakers [12]. The dataset has only mouth regions. GRID, one of the most commonly used datasets, consists of sentences. The sentences in the GRID dataset are formed according to a certain structure. This sentence structure, in which 34 speakers repeat 1000 times, consists of command, color, letter, number and adverb. One of the example sentences is 'lay green by A 4 now' [13]. Table 1 shows the content, class numbers, sample numbers, resolutions, shooting angle and language details of the datasets, which are seen to be created in different structures. Fields marked with + in the table represent the content of the relevant column in the content of that dataset, while fields marked with - indicate that the content of the relevant column is not represented in the content of that dataset. For example, for the AvLetters dataset created in 2002, the Alphabet was used, but word and sentence structures were not used in the dataset. When the data sets in Table 1 are examined, it is seen that the data sets are generally in English. Although most of the studies are on English, it is seen that there are studies in other languages. [PERSON] et al. [15] created their own dataset on Chinese and worked with ShuffleNet and CBAM. In another lip-reading study on Chinese Mandarin, ref. [16] used a data set called CMLR collected on Chinese television. [PERSON] et al. [17], on the other hand, reached state of the art on the Chinese Mandarin dialect with a Transformer-based model. [PERSON] et al. [18] achieved a success rate of 92.67% with a system they proposed with machine learning algorithms on the Korean language. Data sets in other languages such as Spanish have also been created [19; 20] and related lip-reading studies have been conducted. When lip-reading studies are examined, it is seen that many studies have been carried out on the OuluVs2 data set consisting of numbers [21; 22]. [PERSON] et al. [22] used OuluVs2 and different datasets and achieved very high performance using Bidirectional LSTM. [PERSON] et al. [21] achieved 93.72% classification success in the OuluVs2 dataset. When [PERSON] et al. [23] worked on the GRID dataset, it was seen that they reached 95.2% accuracy. It is seen that more than one convolutional neural network is used in the models in lip reading studies [24]. Spatio-temporal data between video frames with multiple