OCR Performance Prediction Using a Bag of Allographs and Support Vector Regression

Bhowmik, Tanmay; Paquet, Thierry; Ragot, Nicolas

doi:10.1109/das.2014.72

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…We compared the proposed method with other state-of-the-art methods including [7,16]. For evaluation, we used images from the well known database of ancient documents presented in [63,64]. This database contains 25 pairs of recto-verso images of ancient manuscripts affected by bleed-through, along with ground truth images.…”

Section: Resultsmentioning

confidence: 99%

“…For the Balinese character dataset, Balinese philologists manually annotated the segment of connected components that represented a correct character in Balinese script from the word-level binarized images that were manually annotated [11,17,20] using Aletheia (http://www.primaresearch. org/tools/Aletheia) [62,63] (Figure 14). The Sundanese character dataset was annotated manually [22] (Figure 15).…”

Section: Datasetsmentioning

confidence: 99%

“…The order of how each character in the word is selected is also kept [21]. Balinese ( Figure 18) and the Sundanese (Figure 19) word dataset was manually annotated using Aletheia [63]. …”

Section: Datasetsmentioning

confidence: 99%

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Document Image Processing

Likforman-Sulem¹,

Kavallieratou²

2018

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Section: Resultsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

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Document Image Processing

Likforman-Sulem¹,

Kavallieratou²

2018

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“…As a consequence, it is very important to be aware of the OCR recognition rate before deciding between one of these three solutions. The amount of recent publications on this subject ( [63][64][65][66]) reflects the scientific interest in predicting OCRs recognition rate.…”

Section: Predict Ocr Recognition Rate Using Synthetic Imagesmentioning

confidence: 99%

“…The difference between the real OCR rate and the one computed on the synthetic versions (Table 2 Column 1 and Column 3) is, on average, only overestimated by 0.03. Most of the success of different existing OCR prediction methods ( [63][64][65][66]) are related to the quality and quantity of the needed ground truth. Our prediction method presented here provides comparable results with the ones form the state of the art.…”

Section: Predict Ocr Recognition Rate Using Synthetic Imagesmentioning

confidence: 99%

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

et al. 2017

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Most digital libraries that provide user-friendly interfaces, enabling quick and intuitive access to their resources, are based on Document Image Analysis and Recognition (DIAR) methods. Such DIAR methods need ground-truthed document images to be evaluated/compared and, in some cases, trained. Especially with the advent of deep learning-based approaches, the required size of annotated document datasets seems to be ever-growing. Manually annotating real documents has many drawbacks, which often leads to small reliably annotated datasets. In order to circumvent those drawbacks and enable the generation of massive ground-truthed data with high variability, we present DocCreator, a multi-platform and open-source software able to create many synthetic image documents with controlled ground truth. DocCreator has been used in various experiments, showing the interest of using such synthetic images to enrich the training stage of DIAR tools.

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Blind document image quality prediction based on modification of quality aware clustering method integrating a patch selection strategy

Alaei

Conte

Martineau

et al. 2018

Expert Systems with Applications

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The quality of document images has direct impacts on the performance of document image processing systems. Document Image Quality Assessment (DIQA) is, therefore, of fundamental importance to a numerous document processing applications. As manual quality assessment is almost impossible for a huge volume of document images generated in day-today life, it is critical to develop intelligent machine operated methods to estimate the quality of document images. In this paper, a blind document image quality assessment method is proposed to deal with the problem of DIQA in real scenarios, as reference images are not always available. To estimate the quality of a document image, the document is rst sampled into a set of patches. The extracted patches are then ltered out based on their level of foreground information using a patch selection strategy. For every selected patch, a cluster assignment is then performed to obtain its quality from a quality aware bag of visual words constructed using k-means clustering. An average pooling is nally employed to estimate the quality of the input document image. To evaluate the proposed method, a dataset composed of document images and three scene image datasets were considered for experimentation. The results obtained from the proposed method demonstrate the eectiveness of the proposed DIQA method. These achievements in applied computational intelligence, expert and decision support systems make a good foundation for creating practical tools to automate document image forgery detection, and archiving process.

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OCR Performance Prediction Using a Bag of Allographs and Support Vector Regression

Cited by 6 publications

References 12 publications

Document Image Processing

Document Image Processing

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

Blind document image quality prediction based on modification of quality aware clustering method integrating a patch selection strategy

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