A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors

Methods for text segmentation from scene images

Camera-captured scene/born-digital image analysis helps in the development of vision for robots to read text, transliterate or translate text, navigate and retrieve search results. However, text in such images does nor follow any standard layout, and its location within the image is random in nature. In addition, motion blur, non-uniform illumination, skew, occlusion and scale-based degradations increase the complexity in locating and recognizing the text in a scene/born-digital image. OTCYMIST method is proposed to segment text from the born-digital images. This method won the first place in ICDAR 2011 and placed in the third position in ICDAR 2013 for its performance on the text segmentation task in robust reading competitions for born-digital image data set. Here, Otsu’s binarization and Canny edge detection are separately carried out on the three colour planes of the image. Connected components (CC’s) obtained from the segmented image are pruned based on thresholds applied on their area and aspect ratio. CC’s with sufficient edge pixels are retained. The centroids of the individual CC’s are used as nodes of a graph. A minimum spanning tree is built using these nodes of the graph. Long edges are broken from the minimum spanning tree of the graph. Pairwise height ratio is used to remove likely non-text components. CC’s are grouped based on their proximity in the horizontal direction to generate bounding boxes (BB’s) of text strings. Overlapping BB’s are removed using an overlap area threshold. Non-overlapping and minimally overlapping BB’s are retained for text segmentation. These BB’s are split vertically to localize text at the word level. A word cropped from a document image can easily be recognized using a traditional optical character recognition (OCR) engine. However, recognizing a word, obtained by manually cropping a scene/born-digital image, is not trivial. Existing OCR engines do not handle these kinds of scene word images effectively. Our intention is to first segment the word image and then pass it to the existing OCR engines for recognition. It is advantageous in two aspects: it avoids building a character classifier from scratch and reduces the word recognition task to a word segmentation task. Here, we propose three bottom-up approaches to segment a cropped word image. These approaches choose different features at the initial stage of segmentation. Power-law transform (PLT) was applied to the pixels of the gray scale born-digital images to non-linearly enhance the histogram. The recognition rate achieved on born-digital word images is 82. 9%, which is 20% more than the top performing entry (61. 5%) in ICDAR 2011 robust reading competition. The recognition rate is 82. 7% and 64. 6% for born-digital and scene images of ICDAR 2013 robust reading competition, respectively, using PLT. In addition, we applied PLT to the colour planes such as red, green, blue, intensity and lightness plane by varying the gamma value. We call this technique as Nonlinear enhancement and selection of plane (NESP) for optimal segmentation, which is an improvement over PLT. NESP chooses a particular plane with a proper gamma value based on Fisher discrimination factor. The recognition rate is 72. 8% for scene images of ICDAR 2011 robust reading competition, which is 30% higher than the best entry (41. 2%). The recognition rate is 81. 7% and 65. 9% for born-digital and scene images of ICDAR 2013 robust reading competition, respectively, using NESP. Another technique, midline analysis and propagation of segmentation (MAPS), has also been proposed for word segmentation. Here, the middle row pixels of the gray scale image are first segmented and the statistics of the segmented pixels are used to assign text and non-text labels to the rest of the image pixels using min-cut method. Gaussian model is fitted on the middle row segmented pixels before the assignment of other pixels. In MAPS method, we assume the middle row pixels are least affected by any of the degradations. This assumption is validated by the good word recognition rate of 71. 7% on ICDAR 2011 robust reading competition for scene images. The recognition rate is 83. 8% and 66. 0% for born-digital and scene images of ICDAR 2013 robust reading competition, respectively, using MAPS. The best reported results for ICDAR 2003 word images is 61. 1% using custom lexicons containing the list of test words. On the other hand, NESP and MAPS achieve 66. 2% and 64. 5% for ICDAR 2003 word images without using any lexicon. By using similar custom lexicon, the recognition rates for ICDAR 2003 word images go up to 74. 9% and 74. 2% for NESP and MAPS methods, respectively. We manually segmented word images and recognized these images using OCR to benchmark maximum possible recognition rate for each database. The recognition rates of the proposed methods and the benchmark results are reported on the seven publicly available word image data sets and compared with the results reported in the literature. We have designed a classifier to recognize Kannada characters and words from Chars74k data set and our own image collection, respectively. Discrete cosine transform (DCT) and block DCT are used as features to train separate classifiers. Kannada words are segmented using the same techniques (MAPS and NESP) and further segmented into groups of components, since a Kannada character may be represented by a single component or a group of components in an image. The recognition rate on Kannada words is reported for different features with and without the use of a lexicon. The obtained recognition performance for Kannada character recognition (11. 4%) is three times the best performance (3. 5%) reported in the literature. This thesis has dealt with the principal aspects of camera captured scene/born-digital text image analysis: text localization, text segmentation, and word recognition. We have benchmarked the recognition rates of five word image data sets. We conducted a multi-script robust reading competition as part of ICDAR 2013. This competition was aimed to determine whether the text localization and segmentation methods were capable of handling any text, independent of the script

Arabic cursive text recognition from natural scene images

© 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers

The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

[EN] This paper presents the `NoisyOffice¿ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.This research was undertaken as part of the project TIN2017-85854-C4-2-R, jointly funded by the Spanish MINECO and FEDER founds.Castro-Bleda, MJ.; España Boquera, S.; Pastor Pellicer, J.; Zamora Martínez, FJ. (2020). The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing. The Computer Journal. 63(11):1658-1667. https://doi.org/10.1093/comjnl/bxz098S165816676311Bozinovic, R. M., & Srihari, S. N. (1989). Off-line cursive script word recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1), 68-83. doi:10.1109/34.23114Plamondon, R., & Srihari, S. N. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84. doi:10.1109/34.824821Vinciarelli, A. (2002). A survey on off-line Cursive Word Recognition. Pattern Recognition, 35(7), 1433-1446. doi:10.1016/s0031-3203(01)00129-7Impedovo, S. (2014). More than twenty years of advancements on Frontiers in handwriting recognition. Pattern Recognition, 47(3), 916-928. doi:10.1016/j.patcog.2013.05.027Baird, H. S. (2007). The State of the Art of Document Image Degradation Modelling. Advances in Pattern Recognition, 261-279. doi:10.1007/978-1-84628-726-8_12Egmont-Petersen, M., de Ridder, D., & Handels, H. (2002). Image processing with neural networks—a review. Pattern Recognition, 35(10), 2279-2301. doi:10.1016/s0031-3203(01)00178-9Marinai, S., Gori, M., & Soda, G. (2005). Artificial neural networks for document analysis and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1), 23-35. doi:10.1109/tpami.2005.4Rehman, A., & Saba, T. (2012). Neural networks for document image preprocessing: state of the art. Artificial Intelligence Review, 42(2), 253-273. doi:10.1007/s10462-012-9337-zLazzara, G., & Géraud, T. (2013). Efficient multiscale Sauvola’s binarization. International Journal on Document Analysis and Recognition (IJDAR), 17(2), 105-123. doi:10.1007/s10032-013-0209-0Fischer, A., Indermühle, E., Bunke, H., Viehhauser, G., & Stolz, M. (2010). Ground truth creation for handwriting recognition in historical documents. Proceedings of the 8th IAPR International Workshop on Document Analysis Systems - DAS ’10. doi:10.1145/1815330.1815331Belhedi, A., & Marcotegui, B. (2016). Adaptive scene‐text binarisation on images captured by smartphones. IET Image Processing, 10(7), 515-523. doi:10.1049/iet-ipr.2015.0695Kieu, V. C., Visani, M., Journet, N., Mullot, R., & Domenger, J. P. (2013). An efficient parametrization of character degradation model for semi-synthetic image generation. Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing - HIP ’13. doi:10.1145/2501115.2501127Fischer, A., Visani, M., Kieu, V. C., & Suen, C. Y. (2013). Generation of learning samples for historical handwriting recognition using image degradation. Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing - HIP ’13. doi:10.1145/2501115.2501123Journet, N., Visani, M., Mansencal, B., Van-Cuong, K., & Billy, A. (2017). DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images. Journal of Imaging, 3(4), 62. doi:10.3390/jimaging3040062Walker, D., Lund, W., & Ringger, E. (2012). A synthetic document image dataset for developing and evaluating historical document processing methods. Document Recognition and Retrieval XIX. doi:10.1117/12.912203Dong, C., Loy, C. C., He, K., & Tang, X. (2016). Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295-307. doi:10.1109/tpami.2015.2439281Suzuki, K., Horiba, I., & Sugie, N. (2003). Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1582-1596. doi:10.1109/tpami.2003.1251151Hidalgo, J. L., España, S., Castro, M. J., & Pérez, J. A. (2005). Enhancement and Cleaning of Handwritten Data by Using Neural Networks. Lecture Notes in Computer Science, 376-383. doi:10.1007/11492429_46Pastor-Pellicer, J., España-Boquera, S., Zamora-Martínez, F., Afzal, M. Z., & Castro-Bleda, M. J. (2015). Insights on the Use of Convolutional Neural Networks for Document Image Binarization. Lecture Notes in Computer Science, 115-126. doi:10.1007/978-3-319-19222-2_10España-Boquera, S., Zamora-Martínez, F., Castro-Bleda, M. J., & Gorbe-Moya, J. (s. f.). Efficient BP Algorithms for General Feedforward Neural Networks. Lecture Notes in Computer Science, 327-336. doi:10.1007/978-3-540-73053-8_33Zamora-Martínez, F., España-Boquera, S., & Castro-Bleda, M. J. (s. f.). Behaviour-Based Clustering of Neural Networks Applied to Document Enhancement. Lecture Notes in Computer Science, 144-151. doi:10.1007/978-3-540-73007-1_18Graves, A., Fernández, S., & Schmidhuber, J. (2007). Multi-dimensional Recurrent Neural Networks. Artificial Neural Networks – ICANN 2007, 549-558. doi:10.1007/978-3-540-74690-4_56Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern Recognition, 33(2), 225-236. doi:10.1016/s0031-3203(99)00055-2Pastor-Pellicer, J., Castro-Bleda, M. J., & Adelantado-Torres, J. L. (2015). esCam: A Mobile Application to Capture and Enhance Text Images. Lecture Notes in Computer Science, 601-604. doi:10.1007/978-3-319-19222-2_5

Efficient Scene Text Detection with Textual Attention Tower

Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multioriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.Comment: Accepted by ICASSP 202