# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 4 Results and Discussion
#### Word Dataset
Following these processes, the lip region was detected during the preprocessing stage, and frames were extracted from the videos by centering on the lip regions. The central point used during the data augmentation process was determined by considering the center of the lip region. While generating the frames, the right and left coordinates of the lip region as well as the nose region coordinates were identified. Based on these coordinates, frames were cropped from the videos by centering the lip region. This approach enabled more focused and clearer analysis of lip movements during the data augmentation phase. In videos where lip movements are critical, this method aimed to enhance spatial accuracy. During data augmentation, each image was rotated 10 degrees to the right and 10 degrees to the left, using the central point as a reference. This process aims to enhance the model's spatial generalization ability by allowing objects in the images to be viewed from different angles. It is particularly beneficial in situations where objects are observed from various perspectives, helping the model make accurate predictions. The tilting process was implemented to diversify the dataset and improve the model's generalization capacity. Additionally, data augmentation helps prevent the model from overfitting during the training phase, enabling more accurate predictions. As an additional data augmentation method, a multi-step preprocessing and augmentation strategy was implemented to standardize input dimensions and enhance the dataset for robust training. In this method, for videos with fewer than 16 frames, missing frames are replaced with a video by iteratively repeating the first and last frames until the required number of frames is reached. This approach minimizes the disruption of temporal flow as the first and last frames often contain critical spatial and temporal information. By using these key frames, the method ensures that the model receives a sequence that remains consistent with the structure of the original video. For videos with between 16 and 32 frames, the number of frames was reduced to 16. The frames were sampled at equal intervals throughout the video duration, ensuring that all temporal segments of the video contribute equally to the reduced sequence. This method avoids over-representation of certain segments, such as the beginning or end of the video, and ensures that salient features spanning the entire temporal range are preserved. For videos with more than 32 frames, a segmentation strategy was applied. To maximize the variability of the dataset while maintaining temporal coherence, the videos were divided into multiple segments of 16 frames each. Specifically, odd-numbered frames were assigned to the first segment and even-numbered frames to the second segment. In cases where the number of frames was large, equal intervals were set and the odd and even numbered frames were split into separate videos, resulting in two videos. For videos with more than 48 frames, the same process was performed to obtain 3 videos. This method effectively utilizes the full temporal resolution of the original video while capturing the dynamics of continuous lip movement and ensures that the temporal sequence in each segment remains intact. Figure 10: Video shooting environment. Combining these approaches, the preprocessing pipeline addresses the challenges of variable frame counts, ensuring that the input data fed into the model is both standardized and representative. After the data augmentation process was completed, the data was stored in AVI format. The number of videos created using the data augmentation methods, such as removing specific regions from the videos and rotating them, is presented in Table 10.  
After the data presented in Table 11 was obtained, the data preprocessing phase was completed, and the training phase began.