November 14, 2024 - The scene text detection and recognition (STDR) pipeline is implemented using state-of-the-art deep learning models, including CRAFT for text detection and PARSeq for text recognition, and is optimized for accuracy and low latency.

Correspondingly, we propose an autonomous, bidirectional and iterative ABINet for scene text recognition. Firstly, the autonomous suggests to block gradient flow between vision and language models to enforce explicitly language modeling. Secondly, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation.

May 23, 2022 - Abstract:Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient.

Union14M provides a harder evaluation with 3.2M real-world samples including curved, occluded, and low-resolution text. Scene text recognition in 2025 has reached high maturity on standard benchmarks — the top methods (PARSeq, ABINet, CLIP4STR) ...

November 14, 2024 - For text recognition, we used the PARseq model, which is trained on various datasets (MJSynth, SynthText, COCO-Text, RCTW17, Uber-Text, ArT, LSVT, MLT19, and ReCTS, TextOCR) and finetuned with in-house data.

June 29, 2023 - Abstract: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.

In this work, we balanced recognition accuracy, model speed, and cost. Based on Vit-tiny, we built a lightweight scene text recognition model (LSTR) and designed position-enhancement and visual-enhancement modules, allowing the model to maintain a fast inference speed while achieving higher accuracy at a lower cost.

December 17, 2023 - This paper presents an end-to-end model for text recognition through refining the multi-views of the same scene, which is called E2EMVSTR (End-to-End Model for Multi-View Scene Text Recognition).

A paper collection of recent diffusion models for text-image generation tasks, e,g., visual text generation, font generation, text removal, text image super resolution, text editing, handwritten generation, scene text recognition and scene text ...

Official PyTorch implementation ... STR models fit into. Using this framework allows for the module-wise contributions to performance in terms of accuracy, speed, and memory demand, under one consistent set of training and evaluation datasets. Such analyses clean up the hindrance on the current comparisons to understand the performance gain of the existing modules. Based on this framework, we recorded the 1st place of ICDAR2013 focused scene text, ICDAR2019 ...

EverMemOS presents a self-organizing memory system for large language models that processes dialogue streams into structured memory cells and scenes to enhance long-term interaction capabilities.

December 18, 2025 - To address these issues, this paper proposes a novel scene text recognition model named the Encoder–Decoder Interactive Model (EDIM). Based on an encoder–decoder framework, EDIM introduces a Multi-scale Dilated Fusion Attention (MSFA) module ...

March 26, 2023 - The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge.

Extracting this text can provide additional context and details about the places the text describes and the information it conveys. This deep learning model is based on the PaddleOCR model and uses optical character recognition (OCR) technology to detect text in images.

EverMemOS presents a self-organizing memory system for large language models that processes dialogue streams into structured memory cells and scenes to enhance long-term interaction capabilities.

June 28, 2023 - Section 2 summarizes the relevant research in this field, with a focus on text recognition in the field of deep learning. In Section 3, the CRNN recognition model, which combines label smoothing and the language model, is introduced in detail. Experimental results and corresponding discussions are provided in Section 4. The paper concludes with a summary in Section 5. As the field of deep learning continues to develop, the direction of scene text recognition has also been developed, and many researchers have proposed many new and relevant recognition algorithms.

arXiv-2023:Improving Scene Text Recognition for Character-Level Long-Tailed Distribution ... Neurocomputing-2023:DPF-S2S: A novel dual-pathway-fusion-based sequence-to-sequence text recognition model

MLT-5k [26] to train the model, which follows the setup of · [38]. The experiments are conducted on 4 NVIDIA 4090 · GPUs with batch size 384 per GPU for 20 epochs. Spe- cially, we directly use the scene text detector provided by · Wang et al. [38] without fine-tuning to crop the text patches. 4.3. Evaluation Metric · We set the size of the recognition character to 36, including ·