Text line detection and localization is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a new approach for full page text recognition. Localization of the text lines is based on regressions with Fully Convolutional Neural Networks and Multidimensional Long Short-Term Memory as contextual layers. In order to increase the efficiency of this localization method, only the position of the left side of the text lines are predicted. The text recognizer is then in charge of predicting the end of the text to recognize. This method has shown good results for full page text recognition on the highly heterogeneous Maurdor dataset.
Other methods follow a top-down approach and split the pages into smaller parts. The XY-cut algorithm @cite_19 looks for vertical and horizontal white spaces to successively split the pages in paragraphs, lines and words. Similarly, projection profile algorithms like @cite_7 are aimed at finding the horizontal whiter parts of a paragraph. This technique is extended to non-horizontal texts by methods like @cite_9 that dynamically finds a path between the text lines or by @cite_1 that use a Viterbi algorithm to minimize this path.
@cite_19 @cite_9 @cite_1 @cite_7
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In this paper, we propose a novel technique to segment handwritten document images into text lines by shredding their surface with local minima tracers. Our approach is based on the topological assumption that for each text line, there exists a path from one side of the image to the other that traverses only one text line. We first blur the image and then use tracers to follow the white-most and black-most paths from left to right as well as from right to left in order to shred the image into text line areas. We experimentally tested the proposed methodology and got promising results comparable to state of the art text line segmentation techniques. Abstract This paper presents a recognition-based character segmentation method for handwritten Chinese characters. Possible non-linear segmentation paths are initially located using a probabilistic Viterbi algorithm. Candidate segmentation paths are determined by verifying overlapping paths, between-character gaps, and adjacent-path distances. A segmentation graph is then constructed using candidate paths to represent nodes and two nodes with appropriate distances are connected by an arc. The cost in each arc is a function of character recognition distances, squareness of characters and internal gaps in characters. After the shortest path is detected from the segmentation graph, the nodes in the path represent optimal segmentation paths. In addition, 125 text-line images are collected from seven form documents. Cumulatively, these text-lines contain 1132 handwritten Chinese characters. The average segmentation rate in our experiments is 95.58 . Moreover, the probabilistic Viterbi algorithm is modified slightly to extract text-lines from document pages by obtaining non-linear paths while gaps between text-lines are not obvious. This algorithm can also be modified to segment characters from printed text-line images by adjusting parameters used to represent costs of arcs in the segmentation graph. The multi-orientation occurs frequently in ancient handwritten documents, where the writers try to update a document by adding some annotations in the margins. Due to the margin narrowness, this gives rise to lines in different directions and orientations. Document recognition needs to find the lines everywhere they are written whatever their orientation. This is why we propose in this paper a new approach allowing us to extract the multi-oriented lines in scanned documents. Because of the multi-orientation of lines and their dispersion in the page, we use an image meshing allowing us to progressively and locally determine the lines. Once the meshing is established, the orientation is determined using the Wigner---Ville distribution on the projection histogram profile. This local orientation is then enlarged to limit the orientation in the neighborhood. Afterward, the text lines are extracted locally in each zone basing on the follow-up of the orientation lines and the proximity of connected components. Finally, the connected components that overlap and touch in adjacent lines are separated. The morphology analysis of the terminal letters of Arabic words is here considered. The proposed approach has been experimented on 100 documents reaching an accuracy of about 98.6 .