PHP: Best way to extract text within parenthesis?

i'd just do a regex and get it over with. unless you are doing enough iterations that it becomes a huge performance issue, it's just easier to code (and understand when you look back on it)

$text = 'ignore everything except this (text)';
preg_match('#((.*?))#', $text, $match);
print $match[1];

I think that the best way to do what you need is to find and isolate the cells in the file and then apply OCR to each individual cell.

There are a number of solutions in SO for that, I got the code from this answer and played around a little with the parameters to get the output below (not perfect yet, but you can tweak it a little bit yourself).

import os
import cv2
import imutils

# This only works if there's only one table on a page
# Important parameters:
#  - morph_size
#  - min_text_height_limit
#  - max_text_height_limit
#  - cell_threshold
#  - min_columns


def pre_process_image(img, save_in_file, morph_size=(23, 23)):

    # get rid of the color
    pre = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Otsu threshold
    pre = cv2.threshold(pre, 250, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    # dilate the text to make it solid spot
    cpy = pre.copy()
    struct = cv2.getStructuringElement(cv2.MORPH_RECT, morph_size)
    cpy = cv2.dilate(~cpy, struct, anchor=(-1, -1), iterations=1)
    pre = ~cpy

    if save_in_file is not None:
        cv2.imwrite(save_in_file, pre)
    return pre


def find_text_boxes(pre, min_text_height_limit=20, max_text_height_limit=120):
    # Looking for the text spots contours
    contours, _ = cv2.findContours(pre, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

    # Getting the texts bounding boxes based on the text size assumptions
    boxes = []
    for contour in contours:
        box = cv2.boundingRect(contour)
        h = box[3]

        if min_text_height_limit < h < max_text_height_limit:
            boxes.append(box)

    return boxes


def find_table_in_boxes(boxes, cell_threshold=100, min_columns=3):
    rows = {}
    cols = {}

    # Clustering the bounding boxes by their positions
    for box in boxes:
        (x, y, w, h) = box
        col_key = x // cell_threshold
        row_key = y // cell_threshold
        cols[row_key] = [box] if col_key not in cols else cols[col_key] + [box]
        rows[row_key] = [box] if row_key not in rows else rows[row_key] + [box]

    # Filtering out the clusters having less than 2 cols
    table_cells = list(filter(lambda r: len(r) >= min_columns, rows.values()))
    # Sorting the row cells by x coord
    table_cells = [list(sorted(tb)) for tb in table_cells]
    # Sorting rows by the y coord
    table_cells = list(sorted(table_cells, key=lambda r: r[0][1]))

    return table_cells


def build_lines(table_cells):
    if table_cells is None or len(table_cells) <= 0:
        return [], []

    max_last_col_width_row = max(table_cells, key=lambda b: b[-1][2])
    max_x = max_last_col_width_row[-1][0] + max_last_col_width_row[-1][2]

    max_last_row_height_box = max(table_cells[-1], key=lambda b: b[3])
    max_y = max_last_row_height_box[1] + max_last_row_height_box[3]

    hor_lines = []
    ver_lines = []

    for box in table_cells:
        x = box[0][0]
        y = box[0][1]
        hor_lines.append((x, y, max_x, y))

    for box in table_cells[0]:
        x = box[0]
        y = box[1]
        ver_lines.append((x, y, x, max_y))

    (x, y, w, h) = table_cells[0][-1]
    ver_lines.append((max_x, y, max_x, max_y))
    (x, y, w, h) = table_cells[0][0]
    hor_lines.append((x, max_y, max_x, max_y))

    return hor_lines, ver_lines


if __name__ == "__main__":
    in_file = os.path.join(".", "test.jpg")
    pre_file = os.path.join(".", "pre.png")
    out_file = os.path.join(".", "out.png")

    img = cv2.imread(os.path.join(in_file))

    pre_processed = pre_process_image(img, pre_file)
    text_boxes = find_text_boxes(pre_processed)
    cells = find_table_in_boxes(text_boxes)
    hor_lines, ver_lines = build_lines(cells)

    # Visualize the result
    vis = img.copy()

    # for box in text_boxes:
    #     (x, y, w, h) = box
    #     cv2.rectangle(vis, (x, y), (x + w - 2, y + h - 2), (0, 255, 0), 1)

    for line in hor_lines:
        [x1, y1, x2, y2] = line
        cv2.line(vis, (x1, y1), (x2, y2), (0, 0, 255), 1)

    for line in ver_lines:
        [x1, y1, x2, y2] = line
        cv2.line(vis, (x1, y1), (x2, y2), (0, 0, 255), 1)

    cv2.imwrite(out_file, vis)

The closest I could get was using preg_split() instead:

$string = <<< STR
Are you looking for a quality real estate company? <s>Josh's real estate firm 
specializes in helping people find homes from [city][State].</s>
<s>Josh's real estate company is a boutique real estate firm serving clients 
locally.</s> In [city][state] I am sure you know how difficult it is
to find a great home, but we work closely with you to give you exactly 
what you need
STR;

print_r(preg_split(':</?s>:is', $string));

And got this output:

Array
(
    [0] => Are you looking for a quality real estate company? 
    [1] => Josh's real estate firm 
specializes in helping people find homes from [city][State].
    [2] => 

    [3] => Josh's real estate company is a boutique real estate firm serving clients 
locally.
    [4] =>  In [city][state] I am sure you know how difficult it is
to find a great home, but we work closely with you to give you exactly 
what you need
)

Except that produces an extra array element (index 2) where there's a newline between the fragments [city][State].</s> and <s>Josh's real estate company.

It'd be trivial to add some code to remove the whitespace matches though, but I'm not sure if you desire that.