arXiv Computer Vision October 19, 2026 By Nuzhat Khan, Ab Al-Hadi Ab Rahman, Mohamad Anuar Kamaruddin, Muhammad Paend Bakht, Sana Fatima, Farhad Khodadadi, Ibrahim Yousef Alshareef

Improving OCR Accuracy Through Multi-Classifier Verification of Visually Similar Characters

Read the original on arXiv Computer Vision →

The paper introduces the Character Verification Model (CVM), a post‑processing framework that uses binary classifiers to resolve visually similar character ambiguities such as 'O'/'0', 'Z'/'2', and 'S'/'5'. CVM operates independently of dictionary checks, making it applicable to non‑lexical data like serial numbers and license plates. Experiments on three OCR engines and ten fonts show accuracy gains of 3.95–14.81 percentage points, an 87.3% correction rate for ambiguous characters, and a low 2.3% false‑correction rate.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Machine Learning

Aug 28

Systematic Literature Review of Machine Learning Models and Applications for Text Recognition

This systematic literature review examines 97 studies on optical character recognition (OCR) from 2015 to 2025, tracing the evolution of AI models, application domains, data types, and linguistic coverage. It identifies key OCR models, evaluates their performance, strengths, and limitations, and highlights unresolved challenges such as limited resources for underrepresented languages, high variability in handwritten text, and constraints in real‑time applications. The review proposes promising approaches—including self‑supervised learning, multimodal AI, AutoML, AI‑assisted postprocessing, TinyML, and joint corpora creation—to enhance OCR accuracy and address these challenges for industrial use.

By Nuzhat Khan, Ab Al-Hadi Ab Rahman, Shahriyar Masud Rizvi, Ibrahim Yousef Alshareef, Muhammad Nadzir Marsono, Muhammad Paend Bakht, Mohd Shahrizal Rusli, Shahidatul Sadiah

computer-vision multimodal

arXiv AI

Jul 7

ClinOCR-Bench: A Comprehensive Clinical Scanned Document Dataset for Optical Character Recognition Model Evaluation

arXiv:2607. 03650v1 Announce Type: cross Abstract: Extracting textual information from scanned medical documents, such as external laboratory reports and manually filled forms, has been a major challenge in modern electronic health records (EHRs).

By Enshuo Hsu, Jin Zhou, Kirk Roberts

llms computer-vision benchmarks

arXiv Computer Vision

Oct 15

TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering

arXiv:2610.19551v1 Announce Type: new Abstract: Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or miss...

By Yiyang Chen, Guo-Hua Wang, Yaoli Liu, Qing-Guo Chen, Zhao Xu, Weihua Luo, Xiujun Ma

llms diffusion computer-vision multimodal benchmarks

arXiv Computer Vision

3d ago

MuSTEL: A Large-Scale Multilingual Synthetic Text-Line Image Dataset for OCR

arXiv:2610.34148v1 Announce Type: new Abstract: Optical character recognition (OCR) systems depend strongly on the scale and diversity of their training data. One approach to document OCR first analy...

By Majid Adibian, Hossein Saberi, Hossein Zeinali

computer-vision

arXiv Machine Learning

Jul 28

When Low CER is Not Enough: An Analysis of Hallucinations in Vision-Language OCR Systems on Historical Uruguayan Documents

arXiv:2607. 24077v1 Announce Type: cross Abstract: Optical Character Recognition (OCR) is a key component in the digitization of historical archives.

By Marina Gardella (CB), Camilo Mari{\~n}o (UDELAR, CB), Diego Belzarena (UDELAR, CB), Ignacio Ram{\'i}rez (UDELAR), Gregory Randall (UDELAR), Jean-Michel Morel (LU - Hong Kong)

llms computer-vision multimodal benchmarks safety

arXiv Computation and Language

Sep 30

From Pixels to Pairs: A Comprehensive Benchmark of LLM-Driven Key-Value Extraction in Noisy Document Settings

The paper introduces a controlled benchmark for evaluating large language models (LLMs) on key‑value pair extraction from documents with varying levels of OCR noise. It tests 136 configurations across five instruction‑tuned open‑weight LLMs, three datasets, and four text‑quality conditions, using deterministic decoding to generate 17,688 document‑level inferences. The study finds that clean‑text performance does not reliably predict real‑world robustness, model rankings can reverse under noisy conditions, and few‑shot demonstrations do not always improve accuracy, highlighting reliability risks in OCR‑to‑LLM pipelines.

By Zahra Anvari

llms computer-vision multimodal benchmarks