# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 4 Proposed Method
### Feature Extraction
#### 4.2.2 Step 2: Feature Extraction  
Feature Extraction of Speeches  
We perform speech recognition using Google's SpeechRecognition library [32]. Audio clips will be converted to wav extension format to match the input requirement of the recognition library. This library supports the recogni  
Figure 8: Video segmentation into objects, speeches, and subtitles. Figure 7: The proposed model of the distributed and parallel computing for feature extraction. 480 min in length, recognizes many available languages, and supports real-time streaming video. In 2017, [PERSON] and [PERSON] [51] compared speech recognition systems, such as Sphinx-4, Microsoft Speech API, and Google Speech API by using some audio recordings selected from many sources. The results showed that Sphinx-4 achieved 37% word error rate (WER), Microsoft Speech API achieved 18% WER, and Google API achieved 9% WER. The experimental results of the study stated that the acoustic modeling and language model of Google are superior. Thus, we perform speech recognition using Google's SpeechRecognition library. This library automatically recognizes sounds, determines which objects are voices, identifies language types, and correctly converts them into corresponding texts. With support from machine learning, a lot of features can be updated and improved continuously, meeting all user requirements, fast processing speed, and easy integration. These are the most potential advantages for the big data video system in this study. Feature Extraction of Subtitles  
The image frames will be converted to normalized, histogram equalized, and sharpened images to increase the accuracy of subtitle recognition. Then, subtitles in these images are extracted as text using the Tesseract OCR library [30,31]. This library is easy to retrain with new fonts, supports multi-language recognition with high accuracy, and easily integrates with multiple platforms. In 2022, [PERSON] [52] presented work on the text extraction accuracy of the five most prominent products (Google Cloud Vision, AWS Textract, Tesseract OCR, ABBYY, and Microsoft Azure). Tesseract OCR has high recognition accuracy, just behind Google Cloud Vision and AWS Textract. However, the disadvantage of Google Cloud Vision is the high cost. AWS Textract cannot recognize handwritten text and does not achieve stable performance with complex handwriting. Meanwhile, Tesseract automatically extracts/recognizes text subtitles from images in video, identifies language types, and correctly converts them into corresponding texts. With support from machine learning, a lot of features can be updated and improved continuously, meeting all user requirements, fast processing speed, and easy integration. Therefore, we choose Tesseract OCR to perform the video subtitle extraction because of these advantages, which are suitable for the big data video system in this study. Feature Extraction of Objects  
Deep neural networks (DNN) have been shown to produce highly effective deep learning models in a diverse group of fields. It is popularly used for object classification in images and videos, as presented in Section 3.3. Thus, we leverage the advantages of these networks to develop three adaptive deep neural network models for object detection. Then, the content features are extracted from the detected objects by two network models of Faster R-CNN and SSD. As a result, these content features are stored in HDFS, as shown in Figure 9.  
Figure 9: General model for object feature extraction from images in video. The proposed network models for object detection are developed from the ResNet, Inception ResNet V2, and MobileNet V2 by making some improvements to their layers to adapt for the feature extraction of objects in images. The architectures of the proposed network models for object detection are shown in Figures 10-12. ResNet ensures information integrity by simply learning the residual between input and output.