Textline Detection

Detect textlines in document images

Introduction

This tool performs border, region and textline detection from document image data and returns the results as PAGE-XML. The goal of this project is to extract textlines of a document in order to feed them to an OCR model. This is achieved by four successive stages as follows:

• Border detection
• Layout detection
• Textline detection
• Heuristic methods

The first three stages are based on pixelwise segmentation.

Border detection

For the purpose of text recognition (OCR) and in order to avoid noise being introduced from texts outside the printspace, one first needs to detect the border of the printed frame. This is done by a binary pixelwise-segmentation model trained on a dataset of 2,000 documents where about 1,200 of them come from the dhSegment project (you can download the dataset from here) and the remainder having been annotated in SBB. For border detection, the model needs to be fed with the whole image at once rather than separated in patches.

Layout detection

As a next step, text regions need to be identified by means of layout detection. Again a pixelwise segmentation model was trained on 131 labeled images from the SBB digital collections, including some data augmentation. Since the target of this tool are historical documents, we consider as main region types text regions, separators, images, tables and background - each with their own subclasses, e.g. in the case of text regions, subclasses like header/heading, drop capital, main body text etc. While it would be desirable to detect and classify each of these classes in a granular way, there are also limitations due to having a suitably large and balanced training set. Accordingly, the current version of this tool is focussed on the main region types background, text region, image and separator.

Textline detection

In a subsequent step, binary pixelwise segmentation is used again to classify pixels in a document that constitute textlines. For textline segmentation, a model was initially trained on documents with only one column/block of text and some augmentation with regards to scaling. By fine-tuning the parameters also for multi-column documents, additional training data was produced that resulted in a much more robust textline detection model.

Heuristic methods

Some heuristic methods are also employed to further improve the model predictions:

• After border detection, the largest contour is determined by a bounding box and the image cropped to these coordinates.
• For text region detection, the image is scaled up to make it easier for the model to detect background space between text regions.
• A minimum area is defined for text regions in relation to the overall image dimensions, so that very small regions that are actually noise can be filtered out.
• Deskewing is applied on the text region level (due to regions having different degrees of skew) in order to improve the textline segmentation result.
• After deskewing, a calculation of the pixel distribution on the X-axis allows the separation of textlines (foreground) and background pixels.
• Finally, using the derived coordinates, bounding boxes are determined for each textline.

Installation

pip install .

Models

In order to run this tool you also need trained models. You can download our pretrained models from here:
https://qurator-data.de/sbb_textline_detector/

Usage

sbb_textline_detector -i <image file name> -o <directory to write output xml> -m <directory of models>

Usage with OCR-D

ocrd-example-binarize -I OCR-D-IMG -O OCR-D-IMG-BIN
ocrd-sbb-textline-detector -I OCR-D-IMG-BIN -O OCR-D-SEG-LINE-SBB \
        -p '{ "model": "/path/to/the/models/textline_detection" }'

Segmentation works on raw RGB images, but retains AlternativeImages from binarization steps, so it's OK to perform binarization first followed by textline detection. The used binarization processor must produce an AlternativeImage for the binarized image, rather than replace the original raw RGB image.