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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Multilingual Text Recognition: A Deep Learning Approach

Multilingual Text Recognition: A Deep Learning Approach

Peng, Liangrui
Yan, Ruijie

Paperback

Series: Springerbriefs in Computer Science

General ComputersProbability & Statistics

Currently unavailable to order

ISBN10: 9819678978
ISBN13: 9789819678976
Publisher: Springer
Published: Jan 3 2026
Pages: 115
Weight: 0.44
Height: 0.31 Width: 6.34 Depth: 9.20
Language: English

Multilingual text recognition is crucial for cross language information acquisition and related applications in the mobile computing era. The core problem is to find efficient representation and decoding methods for multilingual text recognition, including scene text recognition or handwriting recognition tasks.This book introduces a novel deep learning framework termed Primitive Representation Learning for sequence modeling. In contrast to conventional approaches that employ either (1) convolutional neural networks (CNNs) combined with recurrent neural networks (RNNs) and connectionist temporal classification (CTC) for decoding, or (2) attention-based encoder-decoder architectures, the proposed framework offers an alternative paradigm for sequence representation and processing. Primitive representations are learned via global feature aggregation and then transformed into high level visual text representations via a graph convolutional network, which enables parallel decoding for text transcription. Multielement attention mechanism and temporal residual mechanism are further introduced to enhance the utilization of spatial and temporal feature information.

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