Search Results for author:
Found 9 papers, 2 papers with code
JaPOC: Japanese Post-OCR Correction Benchmark using Vouchers
It is essential for automation processing to correctly recognize scanned voucher text, such as the company name on invoices.
Optical Character Recognition
Optical Character Recognition (OCR)
JSTR: Judgment Improves Scene Text Recognition
In this paper, we present a method for enhancing the accuracy of scene text recognition tasks by judging whether the image and text match each other.
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
By leveraging the strengths of existing research in document image understanding and LLMs' superior language understanding capabilities, the proposed model, fine-tuned with multimodal instruction datasets, performs an understanding of document images in a single model.
RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition
Therefore, we propose a deep reinforcement learning localization method for logo recognition (RL-LOGO).
FA Team at the NTCIR-17 UFO Task
The FA team participated in the Table Data Extraction (TDE) and Text-to-Table Relationship Extraction (TTRE) tasks of the NTCIR-17 Understanding of Non-Financial Objects in Financial Reports (UFO).
DTrOCR: Decoder-only Transformer for Optical Character Recognition
Typical text recognition methods rely on an encoder-decoder structure, in which the encoder extracts features from an image, and the decoder produces recognized text from these features.
Ranked #1 on
Handwritten Text Recognition
on IAM
DiffusionSTR: Diffusion Model for Scene Text Recognition
This paper presents Diffusion Model for Scene Text Recognition (DiffusionSTR), an end-to-end text recognition framework using diffusion models for recognizing text in the wild.
Ranked #12 on
Scene Text Recognition
on IIIT5k
A3S: Adversarial learning of semantic representations for Scene-Text Spotting
Scene-text spotting is a task that predicts a text area on natural scene images and recognizes its text characters simultaneously.
Ranked #1 on
Text Spotting
on SCUT-CTW1500
Video Sparse Transformer With Attention-Guided Memory for Video Object Detection
In this paper, we enhance features element-wisely before the object candidate region detection, proposing Video Sparse Transformer with Attention-guided Memory (VSTAM).
Ranked #1 on
Object Detection
on UA-DETRAC
