Text/Non-text Scene Image Classification Using Deep Ensemble Network

Ghosh, Mridul; Chatterjee, Somnath; Mukherjee, Himadri; Sen, Shibaprasad; Obaidullah, Sk Md

doi:10.1007/978-981-16-5207-3_47

Cited by 3 publications

(2 citation statements)

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“…The loss value is computed as combination of confidence of the forecasted bound box and the accuracy of position. An entire loss value (localization loss + confidence loss) can be calculated using equation (1).…”

Section: Methodsmentioning

confidence: 99%

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Intelligent Deep Learning Empowered Text Detection Model from Natural Scene Images

Devi¹,

CN²

2022

International Journal on Advanced Science, Engineering and Information Technology

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The scene Text Recognition process has become a hot research topic and a challenging task owing to the complicated background, varying light intensities, colors, font styles, and sizes. Text extraction from natural scene images encompasses two main processes: text detection and text recognition. The latest advancements in Machine Learning (ML) and Deep Learning (DL) concepts can effectually automate the text detection and recognition process by training the model properly. In this view, this paper presents an Automated DL empowered Text Detection model from Natural Scene Images (ADLTD-NSI). The ADLTD-NSI technique includes two important processes: text detection and text recognition. Firstly, a single shot detector (SSD) with Inception-v2 as a baseline model is employed for text detection, an object detector based on the VGG-16 framework for feature map extraction followed by six convolution layers. Secondly, Convolutional Recurrent Neural Network (CRNN) technique is utilized for the text recognition process. Besides, the recurrent layers in the CRNN model utilize long short-term memory (LSTM) for encoding the sequence of feature vectors. Lastly, Connectionist Temporal Classification (CTC) loss is applied to predict text labels equivalent to the sequences from the recurrent layers. A wide range of experiments was carried out on benchmark COCO datasets, and the results are examined in several aspects. The experimental outcomes showcased the better performance of the ADLTD-NSI technique over the other compared methods with a maximum accuracy of 96.78%.

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Section: Methodsmentioning

confidence: 99%

“…Nowadays, Scene text recognition has become advanced research since it has abundant semantic data [1]. Text reading technique is embedded in several vision tasks like automatic license-plate recognition, autonomous driving, scene understanding, robot navigation, and assistant technologies for the blind.…”

Section: Introductionmentioning

confidence: 99%