VISUAL DESCRIPTION GENERATING SYSTEM BY USING CNN AND BI-LSTM

Image captioning plays a vital role for numerous purposes. The images contains the captions can lead to rapid and descriptively precise image searches and indexing. Unidirectional LSTM (Long Short-Term Memory) decoders, commonly used in existing systems, have limitations in retaining past information, leading to less than optimal results for long sequences In recent years, the advent of deep neural networks has made image captioning a viable concept. This Visual description generator system (VDGS) introduces a model grounded in deep learning to generate captions for input images. The model takes an image as input, employing algorithms such as Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). The CNN component identifies objects within the image, while the Bi-LSTM constructs sentences about the input image, resulting in a caption tailored to the project. The proposed model primarily emphasizes object identification and formulating the most fitting title for input image. This study presents an enhanced Visual Description Generating System (VDGS) using CNN-based feature extraction and Bi‑LSTM‑based caption generation. The novelty lies in integrating bidirectional contextual modelling, improved sequence handling, and enriched semantic alignment between images and text. The system overcomes limitations of unidirectional LSTMs by incorporating forward–backward temporal processing, improving coherence for long descriptions .The novelty of this work lies in combining CNN (InceptionV3) feature extraction with a Bi‑LSTM decoder to capture both past and future contextual cues. This dual‑context modelling strengthens semantic coherence, improves caption fluency, and enhances alignment with visual regions.