Trigram-based algorithms for OCR result correction

Bulatov, Konstantin B.; Manzhikov, Temudzhin; Slavin, Oleg; Faradjev, Igor; Janiszewski, Igor

doi:10.1117/12.2268559

Cited by 2 publications

(1 citation statement)

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“…Statistical N-gram post-processing models can work both at the characters level [116] and at the word level [112]. N-gram models are based on the assumption that the probability of occurrence of different sequences of characters or words in the recognized data is different.…”

Section: Recognition Results Post-processingmentioning

confidence: 99%

Document image analysis and recognition: a survey

et al. 2022

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This paper analyzes the problems of document image recognition and the existing solutions. Document recognition algorithms have been studied for quite a long time, but despite this, currently, the topic is relevant and research continues, as evidenced by a large number of associated publications and reviews. However, most of these works and reviews are devoted to individual recognition tasks. In this review, the entire set of methods, approaches, and algorithms necessary for document recognition is considered. A preliminary systematization allowed us to distinguish groups of methods for extracting information from documents of different types: single-page and multi-page, with text and handwritten contents, with a fixed template and flexible structure, and digitalized via different ways: scanning, photographing, video recording. Here, we consider methods of document recognition and analysis applied to a wide range of tasks: identification and verification of identity, due diligence, machine learning algorithms, questionnaires, and audits. The groups of methods necessary for the recognition of a single page image are examined: the classical computer vision algorithms, i.e., keypoints, local feature descriptors, Fast Hough Transforms, image binarization, and modern neural network models for document boundary detection, document classification, document structure analysis, i.e., text blocks and tables localization, extraction and recognition of the details, post-processing of recognition results. The review provides a description of publicly available experimental data packages for training and testing recognition algorithms. Methods for optimizing the performance of document image analysis and recognition methods are described.

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Section: Recognition Results Post-processingmentioning

confidence: 99%