MULDT: Multilingual Ultra-Lightweight Document Text Detection for Embedded Devices
Recent research on text detection has focused on scenes “in the wild”, while there is still a demand for a fast and high-quality model for the document domain. Since document OCR is often run on embedded devices such as smartphones, scanners or even AR glasses, it also imposes...
Saved in:
| Main Authors: | Alexander Gayer, Vladimir V. Arlazarov |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10715587/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Addressee as a Factor in Document Type Adaptation
by: Marina V. Kosova, et al.
Published: (2024-09-01) -
Document Layout Error Rate (DLER) metric to evaluate image segmentation methods
by: Ari Vesalainen, et al.
Published: (2024-12-01) -
An optimized lightweight real-time detection network model for IoT embedded devices
by: Rongjun Chen, et al.
Published: (2025-01-01) -
DADNet: text detection of arbitrary shapes from drone perspective based on boundary adaptation
by: Jun Liu, et al.
Published: (2024-11-01) -
Optimizing the performance of a server-based classification for a large business document flow
by: O. A. Slavin
Published: (2023-02-01)