OCR using CRNN: A Deep Learning Approach for Text Recognition

Yadav, Aditya; Singh, Sameer; Siddique, Muhammad; Mehta, Nehal N.; Kotangale, Archana

doi:10.1109/incet57972.2023.10170436

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Despite the maturity of optical character recognition (OCR) technology, reading data from screens such as 7 segments and LCD screens possess some challenges that illustrate the ongoing challenges in OCR technology [25]. Also, in terms of precision, a system designed for OCR is noted to be capable of interpreting captured images of hard disk drive and solid-state drive labels with high accuracy, emphasizing the progression towards higher accuracy in OCR systems [26 -27].…”

Section: Literature Reviewmentioning

confidence: 99%

Information Extraction from Product Labels: A Machine Vision Approach

Seitaj,

Elangovan

2024

IJAIA

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This research tackles the challenge of manual data extraction from product labels by employing a blend of computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition (OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food Facts API (Application Programming Interface) for database population and text-only label prediction. The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set. Importantly, our approach enables visually impaired individuals to access essential information on product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep learning and OCR in automating label extraction and recognition.

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Section: Literature Reviewmentioning

confidence: 99%

Information Extraction from Product Labels: A Machine Vision Approach

Seitaj,

Elangovan

2024

IJAIA

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“…The text detection and identification domain for cursive languages like Kannada, Hindi and Tamil is still in the early stages. A noticeable research gap exists for cursive languages like Kannada, Hindi, and Tamil, especially in natural show promise in recognizing cursive texts [18]- [20], the challenge is amplified with texts in various natural scenes due to their complexities and variability in backgrounds, fonts, sizes, and colors. Furthermore, much of the existing literature is confined to studying isolated characters and scripts [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

CRNN model for text detection and classification from natural scenes

Prakash,

Yeliyur Hanumanthaiah,

Bannur Mayigowda

2024

IJ-AI

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<span lang="EN-US">In the emerging field of computer vision, text recognition in natural settings remains a significant challenge due to variables like font, text size, and background complexity. This study introduces a method focusing on the automatic detection and classification of cursive text in multiple languages: English, Hindi, Tamil, and Kannada using a deep convolutional recurrent neural network (CRNN). The architecture combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks for effective spatial and temporal learning. We employed pre-trained CNN models like VGG-16 and ResNet-18 for feature extraction and evaluated their performance. The method outperformed existing techniques, achieving an accuracy of 95.0%, 96.3%, and 96.2% on ICDAR 2015, ICDAR 2017, and a custom dataset (PDT2023), respectively. The findings not only push the boundaries of text detection technology but also offer promising prospects for practical applications.</span>

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Leveraging Transformer-Based OCR Model with Generative Data Augmentation for Engineering Document Recognition

Khallouli,

Uddin,

Sousa-Poza

et al. 2024

Electronics

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The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced an optical character recognition (OCR) system for text detection and recognition that leverages transformer-based generative deep learning models and transfer learning approaches to enhance text recognition accuracy in engineering documents. The proposed system was evaluated on a dataset collected from ships’ engineering drawings provided by a U.S. agency. Experimental results demonstrated that the proposed transformer-based OCR model significantly outperformed pretrained off-the-shelf OCR models.

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OCR using CRNN: A Deep Learning Approach for Text Recognition

Cited by 4 publications

References 10 publications

Information Extraction from Product Labels: A Machine Vision Approach

Information Extraction from Product Labels: A Machine Vision Approach

CRNN model for text detection and classification from natural scenes

Leveraging Transformer-Based OCR Model with Generative Data Augmentation for Engineering Document Recognition

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