Harnessing CRNN Technology for Image-Based Sequence Recognition

Harnessing CRNN Technology for Image-Based Sequence Recognition

Ujwal Lonarkar, Saurabh Bhite, Suraj Ingale, Prof. Sunita Bangal

Ujwal Lonarkar, Department of Technology,  SPPU

Saurabh Bhite, Department of Technology, SPPU

Suraj Ingale, Department of Technology, SPPU

Prof. Sunita Bangal Department of Technology, SPPU

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Abstract - We are studying the challenge of finding text in everyday images using advanced computer techniques, specifically deep learning. Our system can accurately spot and read text in images. It uses Convolutional Recurrent Neural Networks (CRNNs), a type of deep learning model, to locate and understand the text. Instead of relying on manual rules, our model learns from the data to identify text patterns. Our projects unique feature is that our model is trained on single-word images, making it versatile in recognizing different text styles and backgrounds. We trained our model using a large dataset, including the “MJ Synthetic Word Dataset”, which consists of 50,000 out of which we have taken 35000 as training images, 15000 as validation images. The results have been promising, indicating potential real-world applications such as searching for words in large sets of images.

Key Words: Advanced computer techniques, Deep learning, System, Spot and read text, Convolutional Neural Networks (CRNNs), Deep learning model