AUTOMATED VIDEO TEXT EXTRACTION AND SUMMARIZATION SYSTEM USING LSTM NETWORKS

Information Technology

Video summarization is a process that condenses lengthy videos into shorter, more concise versions, retaining the most important content. It involves selecting key frames, segments, or extracting textual transcripts to create a coherent summary. These summaries serve as a timesaving way to access essential information from videos, making it easier for users to quickly understand the video's content without watching it in its entirety. Machine learning plays a pivotal role in video summarization. Algorithms, particularly deep learning models like Long Short-Term Memory (LSTM) networks, are employed to automatically identify significant moments within a video. Machine learning models analyze visual and audio cues, speaker sentiment, and transcript text to determine the most relevant segments. These segments are then stitched together to form a comprehensive and coherent summary, making video summarization an efficient and accessible means to extract valuable insights from videos, all driven by intelligent algorithms and neural networks. In addition to video summarization, another critical aspect of this project is the translation of English text into various Indian regional languages, including Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, and Malayalam. To facilitate this translation process, we utilized Google Translate, which provides a robust framework for converting English text into these languages accurately. Google Translate has limits on the amount of text you can send for translation. If your summary becomes too long, the Google Translate may fail to process it, which explains why it works for shorter videos but fails for longer ones. This component of the project aims to make video content more accessible to a broader audience by providing summaries in native languages. By integrating translation capabilities, users can engage with the summarized content in their preferred language, enhancing comprehension and ensuring that linguistic barriers do not hinder access to important information. The choice of languages reflects the linguistic diversity of India, allowing speakers of different regional languages to benefit from the video content seamlessly. By leveraging machine translation tools, the project not only emphasizes the importance of summarization but also highlights the need for inclusivity in information dissemination across linguistic divides.

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