Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.

Optical recognition of chemical graphics

Abstract: A prototype system for encoding chemical structure diagrams from scanned printed documents is described. The system distinguishes a structure diagram from other printed material on a page image using size and spacing characteristics. It distinguishes line graphics from symbols in an intermediate vectorization stage. Line information is mapped into a connection diagram that represents atomic bonds. Atomic symbols are identified by means of chemical drawing conventions and optical character recognition. The final coded output interfaces to conventional chemistry software for database storage and retrieval, publishing, and modeling. >

OCR error correction using a noisy channel model

Abstract: In this paper, we take a pattern recognition approach to correcting errors in text generated from printed documents using optical character recognition (OCR). We apply a very general, theoretically optimal model to the problem of OCR word correction, introduce practical methods for parameter estimation, and evaluate performance on real data.

Gesture-based selective text recognition

Abstract: An image is displayed on a touch screen. A user's underline gesture on the displayed image is detected. The area of the image touched by the underline gesture and a surrounding region approximate to the touched area are identified. Skew for text in the surrounding region is determined and compensated. A text region including the text is identified in the surrounding region and cropped from the image. The cropped image is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and returns OCR'ed text. The OCR'ed text is outputted.

Fooling OCR Systems with Adversarial Text Images.

Abstract: We demonstrate that state-of-the-art optical character recognition (OCR) based on deep learning is vulnerable to adversarial images. Minor modifications to images of printed text, which do not change the meaning of the text to a human reader, cause the OCR system to "recognize" a different text where certain words chosen by the adversary are replaced by their semantic opposites. This completely changes the meaning of the output produced by the OCR system and by the NLP applications that use OCR for preprocessing their inputs.

A Fast Re-scoring Strategy to Capture Long-Distance Dependencies

Abstract: A re-scoring strategy is proposed that makes it feasible to capture more long-distance dependencies in the natural language. Two pass strategies have become popular in a number of recognition tasks such as ASR (automatic speech recognition), MT (machine translation) and OCR (optical character recognition). The first pass typically applies a weak language model (n-grams) to a lattice and the second pass applies a stronger language model to N best lists. The stronger language model is intended to capture more long-distance dependencies. The proposed method uses RNN-LM (recurrent neural network language model), which is a long span LM, to re-score word lattices in the second pass. A hill climbing method (iterative decoding) is proposed to search over islands of confusability in the word lattice. An evaluation based on Broadcast News shows speedups of 20 over basic N best re-scoring, and word error rate reduction of 8% (relative) on a highly competitive setup.