Stylistic Similarities in Greek Papyri Based on Letter Shapes: A Deep Learning Approach

Marthot-Santaniello, Isabelle; Vu, Manh Tu; Serbaeva, Olga; Beurton-Aimar, Marie

doi:10.1007/978-3-031-41498-5_22

Cited by 1 publication

(1 citation statement)

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“…Although deep learning has made strides in text recognition within natural settings [15], the bulk of the research has primarily concentrated on foreign scripts (such as Greek texts) [16], [17]. The text detection and identification domain for cursive languages like Kannada, Hindi and Tamil is still in the early stages.…”

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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Section: Introductionmentioning

confidence: 99%