DataWhizmadaan/CNN

Lip-Reader

Prompt

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Model description

This research presents an advanced deep learning model for lip reading, utilizing convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to process video frames and predict text transcriptions by mapping lip movements to character sequences. The current implementation shows promising results, yet there is significant potential for expansion and enhancement to create a robust, multi-lingual, and real-time lip-reading solution. The methodology includes extracting and preprocessing video frames and text transcriptions, constructing a TensorFlow data pipeline, and defining a deep neural network architecture. CNNs are employed for feature extraction from video frames, while Bidirectional LSTMs handle the sequence modeling of character predictions. Training utilizes a custom Connectionist Temporal Classification (CTC) loss function, particularly suited for sequence-to-sequence problems like lip reading. The model’s performance is evaluated on test sets and new video files by comparing predictions with ground truth text transcriptions. The current implementation highlights a robust architecture combining CNNs and LSTMs and employs a custom CTC loss function tailored for sequence-to-sequence tasks. It also features an efficient and scalable data pipeline for data loading and preprocessing, accompanied by a well-documented codebase.

Trigger words

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