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.

A Large Chinese Text Dataset in the Wild

Abstract: In this paper, we introduce a very large Chinese text dataset in the wild. While optical character recognition (OCR) in document images is well studied and many commercial tools are available, the detection and recognition of text in natural images is still a challenging problem, especially for some more complicated character sets such as Chinese text. Lack of training data has always been a problem, especially for deep learning methods which require massive training data. In this paper, we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters from 3 850 unique ones annotated by experts in over 30 000 street view images. This is a challenging dataset with good diversity containing planar text, raised text, text under poor illumination, distant text, partially occluded text, etc. For each character, the annotation includes its underlying character, bounding box, and six attributes. The attributes indicate the character’s background complexity, appearance, style, etc. Besides the dataset, we give baseline results using state-of-the-art methods for three tasks: character recognition (top-1 accuracy of 80.5%), character detection (AP of 70.9%), and text line detection (AED of 22.1). The dataset, source code, and trained models are publicly available.

Optical Character Recognition for printed Tamil text using Unicode

Abstract: Optical Character Recognition (OCR) refers to the process of converting printed Tamil text documents into software translated Unicode Tamil Text. The printed documents available in the form of books, papers, magazines, etc. are scanned using standard scanners which produce an image of the scanned document. As part of the preprocessing phase the image file is checked for skewing. Ifthe image is skewed, it is corrected by a simple rotation technique in the appropriate direction. Then the image is passed through a noise elimination phase and is binarized. The preprocessed image is segmented using an algorithm which decomposes the scanned text into paragraphs using special space detection technique and then the paragraphs into lines using vertical histograms, and lines into words using horizontal histograms, and words into character image glyphs using horizontal histograms.Each image glyph is comprised of 32x32 pixels. Thus a database of character image glyphs is created out of the segmentation phase. Then all the image glyphs are considered for recognition using Unicode mapping. Each image glyph is passed through various routines which extract the features of the glyph. The various features that are considered for classification are the character height, character width, the number of horizontal lines (long and short), the number of vertical lines (long and short), the horizontally oriented curves, the vertically oriented curves, the number of circles, number of slope lines, image centroid and special dots. The glyphs are now set ready for classification based on these features. The extracted features are passed to a Support Vector Machine (SVM) where the characters are classified by Supervised Learning Algorithm. These classes are mapped onto Unicode for recognition. Then the text is reconstructed using Unicode fonts.

Noisy text categorization

Abstract: This work presents categorization experiments performed over noisy texts. By noisy, we mean any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g., transcriptions of speech recordings extracted with a recognition system). The performance of a categorization system over the clean and noisy (word error rate between /spl sim/ 10 and /spl sim/ 50 percent) versions of the same documents is compared. The noisy texts are obtained through handwriting recognition and simulation of optical character recognition. The results show that the performance loss is acceptable for recall values up to 60-70 percent depending on the noise sources. New measures of the extraction process performance, allowing a better explanation of the categorization results, are proposed.

Using character shape coding for information retrieval

Abstract: In conventional information retrieval the task of finding users' search terms in a document is simple. When the document is not available in machine readable format, optical character recognition (OCR) can usually be performed. We have developed a technique for performing information retrieval on document images in such a manner that the accuracy has great utility. The method makes generalisations about the images of characters, then performs classification of these and agglomerates the resulting character shape codes into word tokens based on character shape coding. These are sufficiently specific in their representation of the underlying words to allow reasonable performance of retrieval. Using a collection of over 250 Mbytes of document texts and queries with known relevance assessments, we present a series of experiments to determine how various parameters in the retrieval strategy affect retrieval performance and we obtain a surprisingly good result.

Systems and methods for the visually impared

Abstract: Embodiments herein provide/acquire a document in electronic form (e.g., by receiving, copying, retrieving from storage, scanning combined with optical character recognition, etc.) and receive user input regarding visual impairment. In response to one or more levels of user visual impairment, embodiments herein automatically change (for example, immediately after scanning text) an appearance of the document, without requiring any user input, other than the visual impairment input. More specifically, when changing the appearance of the document, embodiments herein can increase the size of characters in the document, change the contrast or coloring of the text and/or background, and provide text-to-speech conversion of the document, thereby (in one embodiment) producing audio output of the text-to-speech conversion in coordination with a corresponding portion of the document being displayed. When changing the appearance of the document, embodiments herein also reformat the document (e.g., around graphic elements) to accommodate the increased size of the characters.