Top Research Papers on OCR

The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets, and outperforms the current state-of-the-art models on the printed, handwritten and scene text recognition tasks.

Analysis SVM for OCR system performance that is providing a good result that is configured with machine learning approach has proved Statistical SVM for OCR system performance that is providing a good result that is configured with machine learning approach.

This paper proposes a practical ultra lightweight OCR system, i.e., PP-OCR, with an overall model size of only 3.5M, and introduces a bag of strategies to either enhance the model ability or reduce the model size.

TextScanner bears three characteristics: it belongs to the semantic segmentation family, as it generates pixel-wise, multi-channel segmentation maps for character class, position and order, and also adopts RNN for context modeling.

This study presents a deep ALPR system designed to be applicable to multinational LPs, mainly based on the you only look once (YOLO) networks, and proposes a layout detection algorithm that can extract the correct sequence of LP numbers from multinational LLP.

A deep learning (DL) system with two convolutional neural network (CNN) architectures (named HMB1 and HMB2); with the appliance of optimization, regularization, and dropout techniques is introduced to serve as a baseline for future research on handwritten Arabic text.

The importance of enhancing quality of OCR results by studying their effects on information retrieval and natural language processing applications is clarified by defining the post-OCR processing problem, illustrating its typical pipeline, and reviewing the state-of-the-art post- OCR processing approaches.

A series of extrinsic assessment tasks are performed using popular, out-of-the-box tools in order to quantify the impact of OCR quality on these tasks, finding a consistent impact resulting from OCR errors on downstream tasks with some tasks more irredeemably harmed by O CR errors.

An intrusion pattern recognition scheme based on Gramian Angular Field (GAF) and convolutional neural network (CNN) for the dual Mach–Zehnder Interference (DMZI) distributed fiber perimeter security system, which has the advantages of fast recognition speed and high recognition accuracy rate.

This review enumerates various important considerations for designing and interpreting cellular and mitochondrial bioenergetics experiments, some common challenges and pitfalls in data interpretation, and some potential “next steps” to be taken that can address these highlighted challenges.

The Potato crop (Solanum tuberosum L.) is one of the most important vegetable food crop grown globally. The yield of potato crop is greatly hampered both in quality and quantity by fungal blight diseases which pose a major threat to the global food security. Late blight caused by Phytophthora infestans and early blight caused by Alternaria solani are the most devastating foliage diseases for potato crops. In reality, the farmers presume such disorders by visualizing mainly the color change in the potato leaves that is usually risky due to subjectivity and huge time consumption. Under such situ...

It is reported that T7 bacteriophage Ocr, a DNA mimic protein that protects the phage from the defensive action of type I restriction–modification systems, is also active against BREX.

A neuro-inspired optical sensor array based on two-dimensional NbS2/MoS2 hybrid films featured remarkable photo-induced conductance plasticity and low electrical energy consumption and was experimentally implemented such that the post-processing could yield a high restoration accuracy.

When it was originally published in a two-volume edition in 1935, Ralph Barton Perry's magisterial work on William James was greeted with much critical acclaim. A briefer one-volume edition was published in 1947 to serve as both a systematic account of James's development and a repository of selections from his unpublished writings. The one-volume work (which forms the basis for this new paperback edition) offers a brief and convenient sourcebook of James's thought, set forth in terms that require no previous familiarity with technical problems of philosophy and psychology. An anthology of wel...

A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these t...

This work uses deep reinforcement learning to learn controllers that achieve goal-directed movements in data-driven generative models of human movement using autoregressive conditional variational autoencoders, or Motion VAEs.

We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance learned in a new weakly supervised way from multi-view imagery. In contrast to previous work, our controllable 3D character displays dynamics, e.g., the swing of the skirt, dependent on skeletal body motion in an efficient data-driven way, without requiring complex physics simulation. Our character model also features a learned dynamic texture model that accounts for photo-realistic motion-dependent appearance details, as well as view-dependent lighting effects. During tr...

The experimental results show that the algorithm proposed in this paper can effectively recognize face masks and realize the effective monitoring of personnel, and YOLOV5, the most powerful objection detection algorithm at present, is effective in the actual environment.

This literature survey gives a concise introduction to ASR and provides an overview of the general architectures dealing with speaker recognition technologies, and upholds the past, present, and future research trends in this area.

With their distinctiveness and stability over time, fingerprints continue to be the most widely used anatomical characteristic in systems that automatically recognize a person's identity. This fully updated third edition provides in-depth coverage of the state-of-the-art in fingerprint recognition readers, feature extraction, and matching algorithms and applications. Deep learning (resurgence beginning around 2012) has been a game changer for artificial intelligence and, in particular, computer vision and biometrics. Performance improvements (both recognition accuracy and speed) for most biome...