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This is what I mean as document text image: enter image description here

I want to label the texts in image as separate blocks and my model should detect these labels as classes.

NOTE:

This is how the end result should be like:enter image description here

The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc.

Work done:

First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text.

Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks.

PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' 
PIXEL_CLS_WEIGHT_bbox_balanced = 'PIXEL_CLS_WEIGHT_bbox_balanced'
PIXEL_NEIGHBOUR_TYPE_4 = 'PIXEL_NEIGHBOUR_TYPE_4'
PIXEL_NEIGHBOUR_TYPE_8 = 'PIXEL_NEIGHBOUR_TYPE_8'

DECODE_METHOD_join = 'DECODE_METHOD_join'


def get_neighbours_8(x, y):
    """
    Get 8 neighbours of point(x, y)
    """
    return [(x - 1, y - 1), (x, y - 1), (x + 1, y - 1), \
        (x - 1, y),                 (x + 1, y),  \
        (x - 1, y + 1), (x, y + 1), (x + 1, y + 1)]


def get_neighbours_4(x, y):
    return [(x - 1, y), (x + 1, y), (x, y + 1), (x, y - 1)]


def get_neighbours(x, y):
    import config
    neighbour_type = config.pixel_neighbour_type
    if neighbour_type == PIXEL_NEIGHBOUR_TYPE_4:
        return get_neighbours_4(x, y)
    else:
        return get_neighbours_8(x, y)

def get_neighbours_fn():
    import config
    neighbour_type = config.pixel_neighbour_type
    if neighbour_type == PIXEL_NEIGHBOUR_TYPE_4:
        return get_neighbours_4, 4
    else:
        return get_neighbours_8, 8



def is_valid_cord(x, y, w, h):
    """
    Tell whether the 2D coordinate (x, y) is valid or not.
    If valid, it should be on an h x w image
    """
    return x >=0 and x < w and y >= 0 and y < h;

#=====================Ground Truth Calculation Begin==================
def tf_cal_gt_for_single_image(xs, ys, labels):
    pixel_cls_label, pixel_cls_weight,  \
    pixel_link_label, pixel_link_weight = \
        tf.py_func(
                    cal_gt_for_single_image, 
                    [xs, ys, labels],
                    [tf.int32, tf.float32, tf.int32, tf.float32]
                   )
    import config
    score_map_shape = config.score_map_shape
    num_neighbours = config.num_neighbours
    h, w = score_map_shape
    pixel_cls_label.set_shape(score_map_shape)
    pixel_cls_weight.set_shape(score_map_shape)
    pixel_link_label.set_shape([h, w, num_neighbours])
    pixel_link_weight.set_shape([h, w, num_neighbours])
    return pixel_cls_label, pixel_cls_weight, \
            pixel_link_label, pixel_link_weight


def cal_gt_for_single_image(normed_xs, normed_ys, labels):
    """
    Args:
        xs, ys: both in shape of (N, 4), 
            and N is the number of bboxes,
            their values are normalized to [0,1]
        labels: shape = (N,), only two values are allowed:
                                                        -1: ignored
                                                        1: text
    Return:
        pixel_cls_label
        pixel_cls_weight
        pixel_link_label
        pixel_link_weight
    """
    import config
    score_map_shape = config.score_map_shape
    pixel_cls_weight_method  = config.pixel_cls_weight_method
    h, w = score_map_shape
    text_label = config.text_label
    ignore_label = config.ignore_label
    background_label = config.background_label
    num_neighbours = config.num_neighbours
    bbox_border_width = config.bbox_border_width
    pixel_cls_border_weight_lambda = config.pixel_cls_border_weight_lambda

    # validate the args
    assert np.ndim(normed_xs) == 2
    assert np.shape(normed_xs)[-1] == 4
    assert np.shape(normed_xs) == np.shape(normed_ys)
    assert len(normed_xs) == len(labels)

#     assert set(labels).issubset(set([text_label, ignore_label, background_label]))

    num_positive_bboxes = np.sum(np.asarray(labels) == text_label)
    # rescale normalized xys to absolute values
    xs = normed_xs * w
    ys = normed_ys * h

    # initialize ground truth values
    mask = np.zeros(score_map_shape, dtype = np.int32)
    pixel_cls_label = np.ones(score_map_shape, dtype = np.int32) * background_label
    pixel_cls_weight = np.zeros(score_map_shape, dtype = np.float32)

    pixel_link_label = np.zeros((h, w, num_neighbours), dtype = np.int32)
    pixel_link_weight = np.ones((h, w, num_neighbours), dtype = np.float32)

    # find overlapped pixels, and consider them as ignored in pixel_cls_weight
    # and pixels in ignored bboxes are ignored as well
    # That is to say, only the weights of not ignored pixels are set to 1

    ## get the masks of all bboxes
    bbox_masks = []
    pos_mask = mask.copy()
    for bbox_idx, (bbox_xs, bbox_ys) in enumerate(zip(xs, ys)):
        if labels[bbox_idx] == background_label:
            continue

        bbox_mask = mask.copy()

        bbox_points = zip(bbox_xs, bbox_ys)
        bbox_contours = util.img.points_to_contours(bbox_points)
        util.img.draw_contours(bbox_mask, bbox_contours, idx = -1, 
                               color = 1, border_width = -1)

        bbox_masks.append(bbox_mask)

        if labels[bbox_idx] == text_label:
            pos_mask += bbox_mask

    # treat overlapped in-bbox pixels as negative, 
    # and non-overlapped  ones as positive
    pos_mask = np.asarray(pos_mask == 1, dtype = np.int32)
    num_positive_pixels = np.sum(pos_mask)

    ## add all bbox_maskes, find non-overlapping pixels
    sum_mask = np.sum(bbox_masks, axis = 0)
    not_overlapped_mask = sum_mask == 1


    ## gt and weight calculation
    for bbox_idx, bbox_mask in enumerate(bbox_masks):
        bbox_label = labels[bbox_idx]
        if bbox_label == ignore_label:
            # for ignored bboxes, only non-overlapped pixels are encoded as ignored 
            bbox_ignore_pixel_mask = bbox_mask * not_overlapped_mask
            pixel_cls_label += bbox_ignore_pixel_mask * ignore_label
            continue

        if labels[bbox_idx] == background_label:
            continue
        # from here on, only text boxes left.

        # for positive bboxes, all pixels within it and pos_mask are positive
        bbox_positive_pixel_mask = bbox_mask * pos_mask
        # background or text is encoded into cls gt
        pixel_cls_label += bbox_positive_pixel_mask * bbox_label

        # for the pixel cls weights, only positive pixels are set to ones
        if pixel_cls_weight_method == PIXEL_CLS_WEIGHT_all_ones:
            pixel_cls_weight += bbox_positive_pixel_mask
        elif pixel_cls_weight_method == PIXEL_CLS_WEIGHT_bbox_balanced:
            # let N denote num_positive_pixels
            # weight per pixel = N /num_positive_bboxes / n_pixels_in_bbox
            # so all pixel weights in this bbox sum to N/num_positive_bboxes
            # and all pixels weights in this image sum to N, the same
            # as setting all weights to 1
            num_bbox_pixels = np.sum(bbox_positive_pixel_mask)
            if num_bbox_pixels > 0: 
                per_bbox_weight = num_positive_pixels * 1.0 / num_positive_bboxes
                per_pixel_weight = per_bbox_weight / num_bbox_pixels
                pixel_cls_weight += bbox_positive_pixel_mask * per_pixel_weight
        else:
            raise ValueError, 'pixel_cls_weight_method not supported:%s'\
                        %(pixel_cls_weight_method)


        ## calculate the labels and weights of links
        ### for all pixels in  bboxes, all links are positive at first
        bbox_point_cords = np.where(bbox_positive_pixel_mask)
        pixel_link_label[bbox_point_cords] = 1


        ## the border of bboxes might be distored because of overlapping
        ## so recalculate it, and find the border mask        
        new_bbox_contours = util.img.find_contours(bbox_positive_pixel_mask)
        bbox_border_mask = mask.copy()
        util.img.draw_contours(bbox_border_mask, new_bbox_contours, -1, 
                   color = 1, border_width = bbox_border_width * 2 + 1)
        bbox_border_mask *= bbox_positive_pixel_mask
        bbox_border_cords = np.where(bbox_border_mask)

        ## give more weight to the border pixels if configured
        pixel_cls_weight[bbox_border_cords] *= pixel_cls_border_weight_lambda

        ### change link labels according to their neighbour status
        border_points = zip(*bbox_border_cords)
        def in_bbox(nx, ny):
            return bbox_positive_pixel_mask[ny, nx]

        for y, x in border_points:
            neighbours = get_neighbours(x, y)
            for n_idx, (nx, ny) in enumerate(neighbours):
                if not is_valid_cord(nx, ny, w, h) or not in_bbox(nx, ny):
                    pixel_link_label[y, x, n_idx] = 0

    pixel_cls_weight = np.asarray(pixel_cls_weight, dtype = np.float32)    
    pixel_link_weight *= np.expand_dims(pixel_cls_weight, axis = -1)

#     try:
#         np.testing.assert_almost_equal(np.sum(pixel_cls_weight), num_positive_pixels, decimal = 1)
#     except:
#         print  num_positive_pixels, np.sum(pixel_cls_label), np.sum(pixel_cls_weight)
#         import pdb
#         pdb.set_trace()
    return pixel_cls_label, pixel_cls_weight, pixel_link_label, pixel_link_weight

#=====================Ground Truth Calculation End====================


#============================Decode Begin=============================

def tf_decode_score_map_to_mask_in_batch(pixel_cls_scores, pixel_link_scores):
    masks = tf.py_func(decode_batch, 
                       [pixel_cls_scores, pixel_link_scores], tf.int32)
    b, h, w = pixel_cls_scores.shape.as_list()
    masks.set_shape([b, h, w])
    return masks



def decode_batch(pixel_cls_scores, pixel_link_scores, 
                 pixel_conf_threshold = None, link_conf_threshold = None):
    import config

    if pixel_conf_threshold is None:
        pixel_conf_threshold = config.pixel_conf_threshold

    if link_conf_threshold is None:
        link_conf_threshold = config.link_conf_threshold

    batch_size = pixel_cls_scores.shape[0]
    batch_mask = []
    for image_idx in xrange(batch_size):
        image_pos_pixel_scores = pixel_cls_scores[image_idx, :, :]
        image_pos_link_scores = pixel_link_scores[image_idx, :, :, :]    
        mask = decode_image(
            image_pos_pixel_scores, image_pos_link_scores, 
            pixel_conf_threshold, link_conf_threshold
        )
        batch_mask.append(mask)
    return np.asarray(batch_mask, np.int32)

# @util.dec.print_calling_in_short
# @util.dec.timeit
def decode_image(pixel_scores, link_scores, 
                 pixel_conf_threshold, link_conf_threshold):
    import config
    if config.decode_method == DECODE_METHOD_join:
        mask =  decode_image_by_join(pixel_scores, link_scores, 
                 pixel_conf_threshold, link_conf_threshold)
        return mask
    elif config.decode_method == DECODE_METHOD_border_split:
        return decode_image_by_border(pixel_scores, link_scores, 
                 pixel_conf_threshold, link_conf_threshold)
    else:
        raise ValueError('Unknow decode method:%s'%(config.decode_method))


import pyximport; pyximport.install()    
from pixel_link_decode import decode_image_by_join

def min_area_rect(cnt):
    """
    Args:
        xs: numpy ndarray with shape=(N,4). N is the number of oriented bboxes. 4 contains [x1, x2, x3, x4]
        ys: numpy ndarray with shape=(N,4), [y1, y2, y3, y4]
            Note that [(x1, y1), (x2, y2), (x3, y3), (x4, y4)] can represent an oriented bbox.
    Return:
        the oriented rects sorrounding the box, in the format:[cx, cy, w, h, theta]. 
    """
    rect = cv2.minAreaRect(cnt)
    cx, cy = rect[0]
    w, h = rect[1]
    theta = rect[2]
    box = [cx, cy, w, h, theta]
    return box, w * h

def rect_to_xys(rect, image_shape):
    """Convert rect to xys, i.e., eight points
    The `image_shape` is used to to make sure all points return are valid, i.e., within image area
    """
    h, w = image_shape[0:2]
    def get_valid_x(x):
        if x < 0:
            return 0
        if x >= w:
            return w - 1
        return x

    def get_valid_y(y):
        if y < 0:
            return 0
        if y >= h:
            return h - 1
        return y

    rect = ((rect[0], rect[1]), (rect[2], rect[3]), rect[4])
    points = cv2.cv.BoxPoints(rect)
    points = np.int0(points)
    for i_xy, (x, y) in enumerate(points):
        x = get_valid_x(x)
        y = get_valid_y(y)
        points[i_xy, :] = [x, y]
    points = np.reshape(points, -1)
    return points

# @util.dec.print_calling_in_short
# @util.dec.timeit
def mask_to_bboxes(mask, image_shape =  None, min_area = None, 
                   min_height = None, min_aspect_ratio = None):
    import config
    feed_shape = config.train_image_shape

    if image_shape is None:
        image_shape = feed_shape

    image_h, image_w = image_shape[0:2]

    if min_area is None:
        min_area = config.min_area

    if min_height is None:
        min_height = config.min_height
    bboxes = []
    max_bbox_idx = mask.max()
    mask = util.img.resize(img = mask, size = (image_w, image_h), 
                           interpolation = cv2.INTER_NEAREST)

    for bbox_idx in xrange(1, max_bbox_idx + 1):
        bbox_mask = mask == bbox_idx
#         if bbox_mask.sum() < 10:
#             continue
        cnts = util.img.find_contours(bbox_mask)
        if len(cnts) == 0:
            continue
        cnt = cnts[0]
        rect, rect_area = min_area_rect(cnt)

        w, h = rect[2:-1]
        if min(w, h) < min_height:
            continue

        if rect_area < min_area:
            continue

#         if max(w, h) * 1.0 / min(w, h) < 2:
#             continue
        xys = rect_to_xys(rect, image_shape)
        bboxes.append(xys)

    return bboxes

Any suggestions?

Is there any approach that is more suitable for the problem I'm trying to solve?

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Try tesseract from Google. They use it internally. There're blog posts here and here.

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If you want something like that, you can try to test it with already existing & online app like this.

If this is what you want, you can create one:

  1. First locate boundaries for text

  2. Convert it to text

This is quite complicated bu you can still use what is called OCR (Optical Character Recognition) and therefore search for it in github and use open source projects as a starter like this.

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  • $\begingroup$ First, I wanted to segment the image into different blocks with its corresponding labels. And then, I will extract the texts along with labels to a text file. $\endgroup$
    – DGS
    Feb 21 '19 at 16:29
  • $\begingroup$ Don't hesitate to use existing open source projects, there is no shame for that :) $\endgroup$
    – LaSul
    Feb 21 '19 at 17:47
  • $\begingroup$ I don't have any problem in extracting texts from image. My primary concern is label and detect the text block elements in the image as mentioned in Note section.. $\endgroup$
    – DGS
    Feb 22 '19 at 6:19

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