作者: 古代 時(shí)間: 2025-3-21 23:04 作者: 開始沒有 時(shí)間: 2025-3-22 01:54
Enhanced Bank Check Security: Introducing a?Novel Dataset and?Transformer-Based Approach for?Detectid signatures as distinct classes within an object detection framework, effectively handling both detection and verification. We employ a DINO-based network augmented with a dilation module to detect and verify signatures on check images simultaneously. Our approach achieves an AP of 99.2 for genuine作者: 腐蝕 時(shí)間: 2025-3-22 05:13 作者: gratify 時(shí)間: 2025-3-22 08:47 作者: 故意 時(shí)間: 2025-3-22 14:25 作者: 故意 時(shí)間: 2025-3-22 17:33 作者: JOG 時(shí)間: 2025-3-22 22:52 作者: Paraplegia 時(shí)間: 2025-3-23 05:12 作者: 沙漠 時(shí)間: 2025-3-23 07:06 作者: 臥虎藏龍 時(shí)間: 2025-3-23 12:31 作者: 擔(dān)憂 時(shí)間: 2025-3-23 15:53
https://doi.org/10.1007/978-1-349-06578-3g the outcome of legal decisions concerning the appellant. In contrast, the LJEE refers to extracting out the phrases/clauses that led to the final decision. To promote research in developing such a system for Pakistani legal documents, this paper also introduces the VerdictVaultPK dataset. The data作者: NATAL 時(shí)間: 2025-3-23 19:36
d signatures as distinct classes within an object detection framework, effectively handling both detection and verification. We employ a DINO-based network augmented with a dilation module to detect and verify signatures on check images simultaneously. Our approach achieves an AP of 99.2 for genuine作者: Campaign 時(shí)間: 2025-3-23 23:36 作者: 蒙太奇 時(shí)間: 2025-3-24 02:44 作者: 秘傳 時(shí)間: 2025-3-24 08:02
other hand, the CFP uses a Local-Fusion Attention mechanism(LFA) to capture Discrepancy information adaptively among different scales. This approach reduces the model’s sensitivity to scale variations and significantly improves its generalization capabilities across diverse document layouts. Further作者: acclimate 時(shí)間: 2025-3-24 14:10 作者: deviate 時(shí)間: 2025-3-24 15:16 作者: 長矛 時(shí)間: 2025-3-24 19:50 作者: lactic 時(shí)間: 2025-3-25 01:59
sent work focuses on occlusions caused by the spotlight effect. We propose a new algorithm, DocLightDetect, which uses image segmentation as a preprocessing step to improve the accuracy of classifying occlusions caused by the spotlight effect in identification documents. The effectiveness of DocLigh作者: Vasoconstrictor 時(shí)間: 2025-3-25 06:23 作者: GREEN 時(shí)間: 2025-3-25 08:11 作者: flimsy 時(shí)間: 2025-3-25 12:49
detection capabilities. This work underscores the importance of OCR confidence scores in improving detection accuracy and reveals substantial disparities in performance between commercial and open-source OCR technologies.作者: 刺穿 時(shí)間: 2025-3-25 19:03 作者: CHANT 時(shí)間: 2025-3-25 22:52 作者: PLUMP 時(shí)間: 2025-3-26 03:48
Error Correction of?Japanese Character-Recognition in?Answers to?Writing-Type Questions Using T5cognition model with confidence scores to learn additional patterns of recognition errors. The experimental results revealed that the answers corrected by the proposed method were closer to the actual answers than those before the correction and data augmentation was effective for the correction model.作者: ARCH 時(shí)間: 2025-3-26 06:46
ion-tuning performance ranges from 11x to 32x of zero-shot performance and from 0.1% to 4.2% over non-instruction (traditional task) finetuning. Despite the gains, these still fall short of human performance (94.36%), implying there’s much room for improvement.作者: Migratory 時(shí)間: 2025-3-26 10:32
Instruction Makes a?Differenceion-tuning performance ranges from 11x to 32x of zero-shot performance and from 0.1% to 4.2% over non-instruction (traditional task) finetuning. Despite the gains, these still fall short of human performance (94.36%), implying there’s much room for improvement.作者: spondylosis 時(shí)間: 2025-3-26 12:53 作者: 被詛咒的人 時(shí)間: 2025-3-26 20:47
on. To our knowledge, our proposed method is the first to integrate recurrent memory mechanisms with the transformer architecture specialized for multi-page document VQA. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance while maintaining a manageable model size.作者: gimmick 時(shí)間: 2025-3-27 00:14
Conference proceedings 2024, during August 30-31, 2024..The 27? full papers presented were carefully reviewed and selected from 43 submissions addressing topics like: document analysis and understanding; retrieval and VQA; layout analysis; document classification; OCR correction and NLP; recognition systems; and historical do作者: custody 時(shí)間: 2025-3-27 01:49 作者: 車床 時(shí)間: 2025-3-27 08:39 作者: sperse 時(shí)間: 2025-3-27 10:13
Enhanced Bank Check Security: Introducing a?Novel Dataset and?Transformer-Based Approach for?Detectio the coexistence of signatures with other textual and graphical elements on real-world documents. Verification systems must first detect the signature and then validate its authenticity, a dual challenge often overlooked by current datasets and methodologies focusing only on verification. To addres作者: 高貴領(lǐng)導(dǎo) 時(shí)間: 2025-3-27 16:27 作者: FILTH 時(shí)間: 2025-3-27 17:59
Instruction Makes a?Differencee-Vision (LV) models for document analysis and predictions on document images, respectively. Usually, deep neural networks for the DocVQA task are trained on datasets lacking instructions. We show that using instruction-following datasets improves performance. We compare performance across document-作者: Pantry 時(shí)間: 2025-3-27 23:08 作者: impale 時(shí)間: 2025-3-28 04:48 作者: 象形文字 時(shí)間: 2025-3-28 07:47
LD-DOC: Light-Weight Domain-Adaptive Document Layout Analysiscument regions under limited data conditions. The LD-DOC model effectively utilizes information from various scale visual features, enhancing its adaptability to feature distributions in scenarios with limited data and thereby improving the accuracy of document region partitioning. Specifically, our作者: 飛鏢 時(shí)間: 2025-3-28 14:04 作者: anniversary 時(shí)間: 2025-3-28 15:04 作者: 妨礙 時(shí)間: 2025-3-28 18:52
Leveraging Semantic Segmentation Masks with?Embeddings for?Fine-Grained Form Classificationssification is impractical for large collections due to its labor-intensive and error-prone nature. To address this, we propose a representational learning strategy that integrates semantic segmentation and deep learning models such as ResNet, CLIP, Document Image Transformer (DiT), and masked auto-作者: 胎兒 時(shí)間: 2025-3-29 00:42
DocLightDetect: A New Algorithm for?Occlusion Classification in?Identification Documentsin the physical realm raises significant challenges. Several entities, including financial institutions, insurance companies, and government services, require photos of documents sent through mobile applications to associate the physical and digital personas. This procedure entails significant compu作者: HEPA-filter 時(shí)間: 2025-3-29 06:05
Confidence-Aware Document OCR Error Detection utility of OCR confidence scores for enhancing post-OCR error detection. Our study involves analyzing the correlation between confidence scores and error rates across different OCR systems. We develop ConfBERT, a BERT-based model that incorporates OCR confidence scores into token embeddings and off作者: Grating 時(shí)間: 2025-3-29 10:09 作者: 破布 時(shí)間: 2025-3-29 13:20
oring of handwritten short descriptive answers in Japanese language exams. We used a deep neural network (DNN)-based handwriting recognizer and a transformer-based automatic scorer without correcting misrecognized characters or adding rubric annotations for scoring. We achieved acceptable agreement 作者: Limpid 時(shí)間: 2025-3-29 16:42
https://doi.org/10.1007/978-1-349-06578-3n. This technological intervention can help streamline and standardize the decision-making process across all levels of courts. One key benefit of developing such a system is that the junior judges can benefit from the collective knowledge stored in the knowledge base, improving their ability to mak作者: 痛打 時(shí)間: 2025-3-29 23:11
o the coexistence of signatures with other textual and graphical elements on real-world documents. Verification systems must first detect the signature and then validate its authenticity, a dual challenge often overlooked by current datasets and methodologies focusing only on verification. To addres作者: Indelible 時(shí)間: 2025-3-30 00:09 作者: choleretic 時(shí)間: 2025-3-30 04:52
e-Vision (LV) models for document analysis and predictions on document images, respectively. Usually, deep neural networks for the DocVQA task are trained on datasets lacking instructions. We show that using instruction-following datasets improves performance. We compare performance across document-作者: 擺動 時(shí)間: 2025-3-30 09:34 作者: 種類 時(shí)間: 2025-3-30 12:35
achine learning, and information retrieval. While many existing TSR methods employ transformer-based models with generally impressive performance, a gap remains in transformer models specifically designed to handle the distinct attributes of table rows and columns. Moreover, there is a lack of robus作者: 被詛咒的人 時(shí)間: 2025-3-30 17:40
cument regions under limited data conditions. The LD-DOC model effectively utilizes information from various scale visual features, enhancing its adaptability to feature distributions in scenarios with limited data and thereby improving the accuracy of document region partitioning. Specifically, our作者: 推測 時(shí)間: 2025-3-30 20:49 作者: 障礙物 時(shí)間: 2025-3-31 04:34
he classification of book genres using text design on book covers. Text images have both semantic information about the word itself and other information (non-semantic information or visual design), such as font style, character color, etc. When we read a word printed on some materials, we receive i作者: Minutes 時(shí)間: 2025-3-31 07:49
ssification is impractical for large collections due to its labor-intensive and error-prone nature. To address this, we propose a representational learning strategy that integrates semantic segmentation and deep learning models such as ResNet, CLIP, Document Image Transformer (DiT), and masked auto-
